ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS

Aynur İncekırık

doi:10.37880/cumuiibf.1940487

TR EN

KRİPTO PARA PİYASASINDA BLOCKCHAİN TABANLI KARBON KREDİ TOKENLERİNİN FİYAT DİNAMİKLERİNİN DERİN ÖĞRENME YÖNTEMLERİYLE ANALİZİ

Abstract

Bu çalışmanın amacı, blockchain tabanlı karbon kredi tokenleri olan Base Carbon Tonne (BCT), Moss Carbon Credit (MCO2) ve KlimaDAO (KLIMA) ile ana akım kripto varlıklar olan Bitcoin (BTC), Ethereum (ETH), Cardano (ADA) ve Solana (SOL) ile spekülatif nitelikli Carboncoin (CARBON)’in günlük kapanış fiyat dinamiklerini derin öğrenme modelleriyle analiz etmeyi amaçlamaktadır. Analizde ayrıca piyasa duyarlılığını temsil eden Fear & Greed Endeksi, çok değişkenli modele girdi olarak dahil edilmiştir. Çalışmada 21.10.2021–01.11.2025 dönemine ait günlük kapanış fiyatları kullanılmış; değişkenler arası ilişkiler Pearson korelasyon analiziyle incelenmiştir. Çalışmada kullanılan veriler Yahoo Finance ve AI Key API üzerinden temin edilmiştir. Sonuçlar, BCT ve MCO2 arasında güçlü pozitif, bu tokenlerin BTC ile aralarında zayıf negatif korelasyon bulunduğunu göstermektedir. Bu bulgu, karbon kredi tokenlerinin ana kripto piyasasından kısmen bağımsız bir yapı sergilediğini ve kendi içinde ise tutarlı bir yapı oluşturduğunu ortaya koymaktadır. Ayrıca, CARBON ile ana kripto varlıklar arasında zayıf ya da orta düzeyde ilişki belirlenirken, Fear & Greed Endeksi’nin BTC, ETH ve SOL ile orta, karbon kredi tokenleri ile zayıf düzeyde ilişkili olduğu görülmüştür. Modelleme sürecinde LSTM, GRU, Transfer-LSTM ve Transfer-GRU mimarileri kullanılmış; veriler kronolojik bütünlük gözetilerek %70 eğitim, %30 doğrulama ve %30 test olarak ayrılmış; modeller MSE, RMSE, MAE, MAPE ve R² ölçütleriyle değerlendirilmiştir. Elde edilen sonuçlar, GRU mimarisinin genel olarak en yüksek tahmin doğruluğu sunduğunu ve BTC, ETH, BCT ve CARBON fiyat tahminlerinde yüksek R² ve düşük hata oranlarıyla öne çıktığını göstermektedir. KLIMA ve Fear & Greed (F&G) Endeksi tahminlerinde ise transfer öğrenme modelleri görece daha iyi performans sergilemiştir. Genel olarak çalışma, derin öğrenme ve transfer öğrenme yaklaşımlarının tokenleştirilmiş karbon kredi piyasalarının fiyat davranışlarını modellemede etkili olduğunu göstermektedir. Burada, transfer öğrenmenin her senaryoda otomatik üstünlük sağlamadığı, ancak belirli varlık gruplarında stratejik katkı sunduğu değerlendirilmektedir. Bu çalışma, dijital ekonomide sürdürülebilir finans araçlarının fiyatlanmasında yapay zekâ temelli modellerin stratejik bir karar destek mekanizması sunduğunu göstermektedir.

Keywords

Ethical Statement

Bu çalışma etik kurul onayı gerektirmez.

Thanks

Thanks to the journal board.

ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS

Abstract

The aim of this study is to analyze the price dynamics of blockchain-based carbon credit tokens, namely Base Carbon Tonne (BCT), Moss Carbon Credit (MCO2), and KlimaDAO (KLIMA) as well as mainstream crypto assets such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Solana (SOL) and the speculative asset Carboncoin (CARBON). In addition, the Fear & Greed Index, which represents investor sentiment, has been incorporated into the model in line with the role of sentiment-driven effects in price formation processes in cryptocurrency markets, as highlighted in the literature. The study utilized daily closing prices from the period October 21, 2021, to November 1, 2025; correlation analyses were performed on raw daily price series using the Pearson correlation method, which was chosen to examine the direction and strength of the linear relationship between variables. Prior to modeling, the dataset was cleaned, Min-Max normalization was applied, and it was split into a 70% training set and a 30% test set while preserving chronological integrity. While the assumption of stationarity in time series is important from the perspective of classical econometric approaches, this study focuses on deep learning-based methods within the scope of nonlinear modeling frameworks. The data used in the study were obtained from Yahoo Finance and the AI Key API. The findings indicate that there are strong internal linkages among carbon credit tokens. In particular, while a strong positive relationship was observed between BCT and MCO2, it was determined that these tokens exhibit a weak negative correlation with Bitcoin. This suggests that carbon credit tokens are only marginally linked to the broader crypto market but form a more cohesive structure within their own ecosystem. Additionally, it was observed that the CARBON asset exhibits relationships ranging from weak to moderate with major crypto assets. The Fear & Greed Index, meanwhile, showed moderate relationships with BTC, ETH, and SOL, and weaker relationships with carbon credit tokens. During the modeling process, LSTM, GRU, Transfer-LSTM, and Transfer-GRU architectures were used; the data was split into 70% training, 30% validation, and 30% test sets while maintaining chronological integrity; the models were evaluated using MSE, RMSE, MAE, MAPE, and R² metrics. The results show that the GRU architecture generally offers the highest prediction accuracy, while transfer learning models perform relatively better in predictions for the KLIMA and Fear & Greed (F&G) Index. Overall, the study demonstrates that deep learning and transfer learning approaches are effective in modeling price behavior in tokenized carbon credit markets. Here, it is assessed that transfer learning does not automatically provide an advantage in every scenario, but offers strategic contributions for specific asset groups. In conclusion, the study demonstrates that AI-based models can be used as a decision-support mechanism in the pricing of sustainable financial instruments in the digital economy.

Keywords

Ethical Statement

This study does not require ethical committee approval.

Thanks

Dergi kuruluna teşekkür ederim.

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Details

Primary Language

English

Subjects

Econometric and Statistical Methods

Journal Section

Research Article

Authors

Aynur İncekırık ^*
0000-0002-5029-6036
Türkiye

Publication Date

July 26, 2026

Submission Date

April 29, 2026

Acceptance Date

June 28, 2026

Published in Issue

Year 2026 Volume: 27 Number: 3

DOI

https://doi.org/10.37880/cumuiibf.1940487

IZ

https://izlik.org/JA73TX58ZE

Cite

RIS / Bibtex

APA

İncekırık, A. (2026). ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 27(3), 872-893. https://doi.org/10.37880/cumuiibf.1940487

AMA

1.İncekırık A. ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2026;27(3):872-893. doi:10.37880/cumuiibf.1940487

Chicago

İncekırık, Aynur. 2026. “ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS”. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi 27 (3): 872-93. https://doi.org/10.37880/cumuiibf.1940487.

EndNote

İncekırık A (July 1, 2026) ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi 27 3 872–893.

IEEE

[1]A. İncekırık, “ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS”, Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 27, no. 3, pp. 872–893, July 2026, doi: 10.37880/cumuiibf.1940487.

ISNAD

İncekırık, Aynur. “ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS”. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi 27/3 (July 1, 2026): 872-893. https://doi.org/10.37880/cumuiibf.1940487.

JAMA

1.İncekırık A. ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2026;27:872–893.

MLA

İncekırık, Aynur. “ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS”. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, vol. 27, no. 3, July 2026, pp. 872-93, doi:10.37880/cumuiibf.1940487.

Vancouver

1.Aynur İncekırık. ANALYSIS OF PRICE DYNAMICS OF BLOCKCHAIN-BASED CARBON CREDIT TOKENS IN THE CRYPTOCURRENCY MARKET USING DEEP LEARNING METHODS. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2026 Jul. 1;27(3):872-93. doi:10.37880/cumuiibf.1940487