DÜŞÜK KARBONLU TEKNOLOJİ TİCARETİ VE İKLİM DİNAMİKLERİ: MAKİNE ÖĞRENİMİNE DAYALI BİR ARAŞTIRMA

Yıl 2025, Cilt: 10 Sayı: 20, 681 - 706, 27.12.2025

Zozik Sabah , Şakir İşleyen , Yıldırım Demir

https://doi.org/10.54831/vanyyuiibfd.1840750

Öz

İklim değişikliği, Dünya’nın genel iklim modellerinde önemli ve kalıcı değişiklikleri kapsamaktadır. Bu çalışmanın temel amacı, Türkiye, Amerika Birleşik Devletleri, Almanya, Irak ve Çin ülkeleriyle ilgili atmosferik CO₂ konsantrasyonları ve ortalama deniz seviyesindeki değişim verilerinin incelemektir. Ayrıca, bu ülkelerdeki hava sıcaklığı, yüzey sıcaklığı ve hava olaylarına bağlı afetler gibi çeşitli faktörlerle ilişkiler kurmayı amaçlamaktadır. Araştırmada, Uluslararası Para Fonu’nun resmi web sitesinden elde edilen veriler kullanılmıştır. Çalışmada, Python tabanlı bir program aracılığıyla yürütülen Shapley Toplamsal Açıklama metodolojisi ve doğrusal olmayan dış girdi otoregresif ağı kullanılmaktadır. Sonuç olarak, ABD’nin düşük karbonlu teknolojilerle yaptığı ticaretin küresel sıcaklık artışıyla bağlantılı olduğu, buna karşılık çevre dostu ürünlerin ihracatının atmosferdeki CO₂ seviyelerini düşürmeye yardımcı olduğu tespit edilmiştir. Düşük karbonlu ürün ticareti aynı zamanda deniz seviyesinin yükselmesiyle de ilişkili olduğu belirlenmiştir. Modeller yüksek doğruluk göstererek, bu yöntemlerin iklim değişikliklerini tahmin etmek ve politika belirlemek için kullanılmasını desteklemektedir.

Anahtar Kelimeler

Atmosferik CO₂ konsantrasyonları , Büyük veri , İklim değişikliği , Deniz seviyesi değişimi , Makine öğrenimi

LOW-CARBON TECHNOLOGY TRADE AND CLIMATE DYNAMICS: A MACHINE LEARNING-BASED INVESTIGATION

Öz

Climate change encompasses significant and lasting alterations in the Earth’s overall climate patterns. The primary objective of this study is to examine atmospheric CO₂ concentration and mean sea level change data for Turkey, the United States, Germany, Iraq, and China. It also aims to establish relationships with various factors such as air temperature, surface temperature, and weather-related disasters in these countries. Data obtained from the official website of the International Monetary Fund were used in the research. The study employs the Shapley Additive Explanatory methodology and a nonlinear external input autoregressive network, implemented through a Python-based program. The results show that trade in low-carbon technologies by the US is linked to global temperature increase, while exports of environmentally friendly products help reduce atmospheric CO₂ levels. Trade in low-carbon products was also found to be associated with sea level rise. The models demonstrate high accuracy, supporting the use of these methods for predicting climate change and formulating policies.

Anahtar Kelimeler

Big data , Climate change , Atmospheric Co₂ concentrations , Machine learning , Sea level change

Kaynakça

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