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

Authors : Zozik Sabah, Şakir İşleyen, Yıldırım Demir

Pages : 681-706

Doi:10.54831/vanyyuiibfd.1840750

View : 87 | Download : 218

Publication Date : 2025-12-27

Article Type : Research Paper

Abstract :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.
Keywords : Atmosferik CO₂ konsantrasyonları, Büyük veri, İklim değişikliği, Deniz seviyesi değişimi, Makine öğrenimi

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