Determinants of chemical wood pulp production in Europe: evidence from panel econometrics and machine learning
Yazarlar (1)
Dr. Öğr. Üyesi Hakan AYDOĞAN Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | European Journal of Forest Research (Q1) | ||
| Dergi ISSN | 1612-4669 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2026 |
| Kabul Tarihi | 26-05-2026 | Yayınlanma Tarihi | – |
| Cilt / Sayı / Sayfa | 145 / 4 / – | DOI | 10.1007/s10342-026-01907-4 |
| Makale Linki | https://link.springer.com/article/10.1007/s10342-026-01907-4 | ||
| UAK Araştırma Alanları |
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| Özet |
| Chemical wood pulp production plays a strategic role in Europe’s forest-based bioeconomy, given its strong linkages with energy consumption, industrial structure, and international trade. Understanding the key determinants of production is therefore essential for designing policies that support both industrial competitiveness and sustainability. This study aims to identify the macroeconomic, structural, trade, and energy-related drivers of chemical wood pulp production across Europe, using a balanced panel of eleven countries over the period 2009–2023. To capture both structural relationships and potential nonlinearities, the analysis integrates panel data econometric techniques with machine learning methods. Specifically, conventional panel regression models are complemented by an Extreme Gradient Boosting (XGBoost) framework, with Shapley Additive Explanations (SHAP) employed to enhance model … |
| Anahtar Kelimeler |
| Chemical wood pulp | Energy intensity | Europe | Industrial structure | Pulp and paper industry | Trade dynamics |
BM Sürdürülebilir Kalkınma Amaçları
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| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 2 |