Green Bonds in Forestry Finance: A Quantile-on-Quantile and Machine Learning Perspective on Environmental and Financial Drivers

Abstract

Green bonds (GBs) have emerged as a prominent sustainable finance instrument, yet their role in funding timber and forestry (TF) projects remains underexplored. This study provides the first empirical assessment of the financial significance of GBs within the TF sector, addressing critical questions about their alignment with forestry’s risk–return profile. Using daily data we analyze the dynamic and nonlinear interactions between timber and forestry returns and green bonds performance. We also include control variables that might have significant impact on timber and forestry return series. Our multi-stage methodology integrates Quantile Cointegration, Quantile-on-Quantile analysis, and Random Forest machine learning to capture distributional heterogeneity, asymmetric effects in financial time series. Our results show that the green bonds have only a modest and asymmetric impact on timber and forestry, which is becoming significant mainly at distributional extremes. Random Forest analysis identifies GB performance and water availability as key drivers of TF returns, while carbon markets exert more moderate influences. This limited role of green bonds reflects persistent sector risks, including long project durations, changing environmental conditions and a lack of standardized metrics. Our findings indicate that key control variables such as water availability and carbon credits have significant, widespread effects on TF returns. By bridging econometric and machine learning approaches, this research advances methodological innovation in green finance studies and offers actionable insights for policymakers, investors, and forest sector stakeholders seeking to align capital flows with ecological outcomes.

Keywords

• green bonds
• forestry finance
• Machine Learning
• Quantile-on-Quantile approach
• carbon credits
• asymmetric effects
• distributional extremes

Orman Finansmanında Yeşil Tahviller: Çevresel ve Finansal Belirleyicilere Nicelik-Üzerine-Nicelik ve Makine Öğrenmesi Perspektifinden Bir Bakış

Öz

Yeşil tahviller (Green Bonds, GB’ler) sürdürülebilir finansmanın önde gelen araçlarından biri olarak ortaya çıkmıştır; ancak ormancılık ve kereste (Timber and Forestry, TF) projelerinin finansmanındaki rolleri hâlâ yeterince araştırılmamıştır. Bu çalışma, TF sektöründe GB’lerin finansal önemine ilişkin ilk ampirik değerlendirmeyi sunmakta ve yeşil tahvillerin ormancılığın risk-getiri profiliyle uyumuna dair kritik soruları ele almaktadır. Günlük veriler kullanılarak, ormancılık getirileri ile yeşil tahvil performansı arasındaki dinamik ve doğrusal olmayan ilişkiler incelenmiştir. Ayrıca, TF getiri serilerini önemli ölçüde etkileyebilecek kontrol değişkenleri de analize dâhil edilmiştir. Çok aşamalı metodolojimiz, Nicelik Eşbütünleşme (Quantile Cointegration), Nicelik-Üzerine-Nicelik (Quantile-on-Quantile) analizi ve Rassal Orman (Random Forest) makine öğrenmesi tekniklerini entegre ederek finansal zaman serilerindeki dağılımsal heterojenliği ve asimetrik etkileri yakalamaktadır. Elde edilen bulgular, yeşil tahvillerin ormancılık ve kereste piyasaları üzerinde yalnızca sınırlı ve asimetrik bir etkiye sahip olduğunu, bu etkinin ise esas olarak dağılımın uç noktalarında belirginleştiğini göstermektedir. Rassal Orman analizi, GB performansı ile su kaynaklarının mevcudiyetini TF getirilerinin temel belirleyicileri olarak tanımlarken, karbon piyasalarının ise daha ılımlı bir etki yarattığını ortaya koymaktadır. Yeşil tahvillerin bu sınırlı rolü, uzun proje süreleri, değişen çevresel koşullar ve standartlaştırılmış ölçütlerin eksikliği gibi sektörel risklerin devam ettiğini yansıtmaktadır. Sonuçlar ayrıca, su mevcudiyeti ve karbon kredileri gibi temel kontrol değişkenlerinin TF getirileri üzerinde belirgin ve yaygın etkiler yarattığını göstermektedir. Ekonometrik ve makine öğrenmesi yaklaşımlarını birleştiren bu çalışma, yeşil finans araştırmalarında metodolojik yenilik sunmakta ve sermaye akışlarını ekolojik sonuçlarla uyumlu hale getirmeyi amaçlayan politika yapıcılar, yatırımcılar ve orman sektörü paydaşları için uygulanabilir içgörüler sunmaktadır.

Anahtar Kelimeler

• yeşil tahviller
• orman finansmanı
• makine öğrenmesi
• Nicelik-üzerine-Nicelik yaklaşımı
• karbon kredileri
• asimetrik etkiler
• dağılımsal uçlar

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