LOW-CARBON TECHNOLOGY TRADE AND CLIMATE DYNAMICS: A MACHINE LEARNING-BASED INVESTIGATION
Düzeltme Notu
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
- Adamo, N., Al-Ansari, N., Sissakian, V., Fahmi, K. J., & Abed, S. A. (2022). Climate change: Droughts and increasing desertification in the Middle East, with special reference to Iraq. Engineering, 14(7), 235–273.
- Al Iqbal, M. R., Rahman, S., Nabil, S. I., & Chowdhury, I. U. A. (2012, December). Knowledge based decision tree construction with feature importance domain knowledge. In 2012 7th International Conference on Electrical and Computer Engineering (pp. 659–662). IEEE.
- Baker, A. C., Glynn, P. W., & Riegl, B. (2008). Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuarine, Coastal and Shelf Science, 80(4), 435–471.
- BBC News. (2015). The birth of the weather forecast. Retrieved from https://www.bbc.co.uk/news
- Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Mrabet Bellaaj, N. (2018). A nonlinear autoregressive exogenous (NARX) neural network model for predicting daily direct solar radiation. Energies, 11, 620.
- Bulkeley, H. (2002). Part III: Urban governance and sustainability. Retrieved from .http://siteresources.worldbank.org/INTUWM/Resources/3402321205330656272/4768406-1291309208465/PartIII.pdf
- Dunjko, V., & Briegel, H. J. (2018). Machine learning & artificial intelligence in the quantum domain: A review of recent progress. Reports on Progress in Physics, 81(7), 074001.
- Hausken, K., & Mohr, M. (2001). The value of a player in n-person games. Social Choice and Welfare, 18, 465–483.
- Kelle, A. C. (2021). MQTT protokolüne uygulanan siber saldırıların analizleri (Doktora tezi). Marmara Üniversitesi, Türkiye.
- Khaleghi, S., Karimi, D., Beheshti, S. H., Hosen, M. S., Behi, H., Berecibar, M., & Van Mierlo, J. (2021). Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network. Applied Energy, 282, 116159.