Greenhouse Gas Emission Estimation by Artificial Intelligence

Yıl 2024, Cilt: 14 Sayı: 2, 103 - 114

Serkan Ertuğrul

Öz

Human activities, particularly the burning of fossil fuels (such as coal, oil, and natural gas) for energy production, industrial processes, transportation, and deforestation, release significant amounts of greenhouse gases into the atmosphere. Global agreements such as the Paris Agreement have started expressing the goal of reducing human activities and achieving net zero emissions. It is expected that all countries will set targets and work towards reducing greenhouse gas emissions by implementing sustainable and realistic programs. By utilizing data such as financial indicators, population, deforestation, Human Development Index (HDI), and energy consumption, machine learning methods were employed to calculate future greenhouse gas emission levels in some countries. For this purpose, a comparison was made by using deep learning methods, such as Long Short-Term Memory (LSTM) and a hybrid CNN-RNN model, separately with the help of the MATLAB program. Additionally, future greenhouse gas emission predictions were made by comparing the results of the study using LSTM modeling with the predictions obtained through NARX modeling for time-series data. The aim was to emphasize the need for countries to develop sustainable programs by considering various data in order to achieve their greenhouse gas emission reduction targets.

Anahtar Kelimeler

Deep Learning, Climate Change, Carbon, Global Warming, Regression, Greenhouse Gas, Artificial Neural Networks

Kaynakça

• [1] KORKMAZ, K., Küresel Isınma ve Tarımsal Uygulamalara Etkisi. Alatarım dergisi, 2007, 6.2: 43-49.
• [2] Levinson, D. (2020). Logistic Curve Models of CO2 Accumulation. Findings.
• [3] Ayyıldız, B. (2013). Ekolojik ekonomi yaklaşımı ile Türkiye’de çevresel etkinlik analizi (Master's thesis, Gaziosmanpaşa Üniversitesi, Fen Bilimleri Enstitüsü).
• [4] Şahin Ü., Tör O. B., Teimourzadeh S., Demirkol K., Künar A., Voyvoda E., Yeldan E., 2022, TÜRKİYE’NİN KARBONSUZLAŞMA YOL HARİTASI: 2050’DE NET SIFIR
• [5] Wikimedia Foundation, Inc., 2023, Access URL: https://en.wikipedia.org/wiki/Charles_David_Keeling, [Accessed: Feb. 15,2023]
• [6] DİKEN, G. (2020). Antropojenik iklim değişikliğinin balıkçılık ve su ürünleri üzerine etki ve yönetim stratejilerine genel bir bakış. Journal of Anatolian Environmental and Animal Sciences, 5(3), 295-303.
• [7] United Nations Resmi Web Sitesi, 2022, Access URL: https://www.un.org/en/climatechange/paris-agreement , [Accessed: Jun. 08, 2022]
• [8] Aydın, S. G. ve Aydoğdu, G., 2022, Makine Öğrenmesi Algoritmaları Kullanılarak Türkiye ve AB Ülkelerinin CO2 Emisyonlarının Tahmini, Avrupa Bilim ve Teknoloji Dergisi, (37), 42-46.
• [9] Uzlu, E., 2021, Estimates of Greenhouse Gas Emission in Turkey with Grey Wolf Optimizer Algorithm-Optimized Artificial Neural Networks, Neural Computing and Applications, 33(20), 13567-13585.
• [10] Acheampong, A. O. and Boateng, E. B., 2019, Modelling carbon emission intensity: Application of artificial neural network, Journal of Cleaner Production, 225, 833-856.
• [11] Ali, N., Assad, M. E. H., Fard, H. F., Jourdehi, B. A., Mahariq, I. and Al-Shabi, M. A., 2022, CO2 Emission Modeling of Countries in Southeast of Europe by Using Artificial Neural Network, In Sensing for Agriculture and Food Quality and Safety XIV, Vol. 12120, 100-104.
• [12] Komeili Birjandi, A., Fahim Alavi, M., Salem, M., Assad, M. E. H. and Prabaharan, N., 2022, Modeling Carbon Dioxide Emission of Countries in Southeast of Asia by Applying Artificial Neural Network, International Journal of Low-Carbon Technologies, 17, 321-326.
• [13] European Union, Official Website, Access URL: https://edgar.jrc.ec.europa.eu/dataset_ghg70#p3, [Accessed: Feb. 24, 2023]
• [14] UNDP (United Nations Expanded Programme), 2023, Access URL: https://hdr.undp.org/data-center/human-development-index#/indicies/HDI, [Accessed: Feb. 17, 2023]
• [15] Organisation for Economic Co-operation and Development, 2023, Access URL: https://stats.oecd.org/index.aspx?queryid=6779, [Accessed: Feb. 20, 2023]
• [16] Organisation for Economic Co-operation and Development, 2023, Access URL: https://data.oecd.org/natincome/net-national-income.htm, [Accessed: Feb. 19, 2023]
• [17] Birleşmiş Milletler, 2023, Access URL: https://unstats.un.org/unsd/snaama/Basic, [Accessed: Feb. 19, 2023]
• [18] TUNALI, Ç. B., 2011, Uluslararası Para Fonu’nun Kredilendirme Mekanizması: Düşük Gelirli Ülkelere Yönelik Bir İnceleme. Maliye Araştırma Merkezi Konferansları, (56), 69-93.
• [19] Organisation for Economic Co-operation and Development, 2023, Access URL: https://stats.oecd.org/index.aspx?queryid=6779, [Accessed: Feb. 20, 2023]
• [20] Kaya, K., & Koç, E., 2015, Enerji Kaynakları-Yeni̇lenebi̇li̇r Enerji̇ Durumu. Mühendis ve Makina, 56(668), 36-47.
• [21] Organisation for Economic Co-operation and Development, 2023, Access URL: https://data.oecd.org/energy/primary-energy-supply.htm#:~:text=Primary%20energy%20supply%20is%20defined,plus%20or%20minus%20stock%20changes, [Accessed: Feb. 21, 2023]
• [22] Organisation for Economic Co-operation and Development, 2023, Access URL: https://data.oecd.org/energy/renewable-energy.htm#indicator-chart, [Accessed: Feb. 21, 2023]
• [23] U.S. Energy Information Administration (EIA), 2023, Access URL: https://www.eia.gov/totalenergy/data/monthly/index.php, [Accessed: Feb. 20, 2023]
• [24] The World Bank, 2023, Access URL: https://www.worldbank.org/en/topic/forests/forests-area#4, [Accessed: Feb. 19, 2023]
• [25] Alizadeh, M. (2011). Yapay Sinir Ağları İle Fiyat Tahmin Analizi. İstanbul Üniversitesi. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul, 90s.
• [26] Dandil, E. & Serin, Z., 2020, Derin Sinir Ağları Kullanarak Histopatolojik Görüntülerde Meme Kanseri Tespiti, Avrupa Bilim ve Teknoloji Dergisi, 451-463.
• [27] Sanchez, H., 2023, Time Series Forecasting Using Hybrid CNN – RNN, MATLAB Central File Exchange, Retrieved April 24, 2023.
• [28] Alizadeh, M., 2011, Yapay Sinir Ağları İle Fiyat Tahmin Analizi, Yüksek Lisans Tezi, İstanbul Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 90s.