Browsing by Author "Onat, Nuri C."

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Citation - WoS: 25
Citation - Scopus: 30
A model for estimating the carbon footprint of maritime transportation of Liquefied Natural Gas under uncertainty
(Elsevier B.V., 2021) Saleh Aseel; Hussein Al-Yafei; Murat Küçükvar; Nuri Cihat Cihat Onat; Metin Türkay; Yigit Kazancoglu; Ahmed Al-Sulaiti; Abdulla Radi Al-Hajri; Kucukvar, Murat; Onat, Nuri C.; Turkay, Metin; Aseel, Saleh; Kazancoglu, Yigit; Al-Yafei, Hussein; Al-Hajri, Abdulla
The demand for Liquefied Natural Gas (LNG) in the global markets has changed significantly. As a result industries have been forced to consider investing significantly in supply chains to achieve an efficient distribution of LNG for cost efficiency and carbon footprint reduction. To minimize the contribution of LNG maritime transportation to global climate change there is a need to quantify the carbon footprints systematically. In this research we developed a novel and practical model for estimating the carbon footprint for LNG maritime transport. Using the MATLAB program an uncertainty-based carbon footprint accounting framework is created. The Monte Carlo simulation model is built to conduct a carbon footprint analysis while the main input parameters were changed within a reliable range. Later a multivariate sensitivity analysis is performed using the Risk Solver software to estimate the most significant parameters on the net carbon footprints. The sensitivity analysis results showed that that steam process day and steaming fuel consumption are found to be the most sensitive parameters for the overall carbon footprint for both Laden and Ballast trips. Furthermore it was found that the Q-Max vessel produces more carbon emissions when compared to the Q-Flex although both are traveling the same distance and are using the same fuel type. The type of fuel is also significantly affecting the emission values due to the relevant carbon content in the fuel. Like the case of the two conventional vessels the one that is running with the only LNG is found to have fewer emissions when compared to the one run with dual-mode. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Carbon Footprint of Food Production: A Systematic Review and Meta-Analysis
(Nature Portfolio, 2025) Onat, Nuri C.; Kucukvar, Murat; Kazançoğlu, Yiğit; Jabbar, Rateb; Al-Quradaghi, Shimaa; Al-Thani, Soud; Mandouri, Jafar
In the face of the urgent climate crisis, food production is a significant contributor to greenhouse gas emissions (GHG). We analyzed 118 life-cycle assessment (LCA) studies on GHG emissions of food production, considering LCA methods, life cycle phase, waste inclusion, and regional factors, including country, continent, and development status. Additionally, machine learning analysis identifies influential factors of GHG emissions of food production across seven categories: red meats, seafood, white meat, fruits & vegetables, animal products, other plant-based, and others (oils). Based on the gradient boosting algorithm, the LCA method choice ranks among the top determinants for GHG emissions in animal products, red meat, seafood, other plant-based products, and others food categories. Only 22% of studies include waste, revealing up to 39% higher emissions in some categories compared to those excluding waste. Our meta-analysis presents min-max-average GHG emission results for each food category, within countries, different scope settings, waste considerations, and LCA methods.
Citation - WoS: 5
Citation - Scopus: 5
Transforming Challenges into Opportunities for Qatar’s Food Industry: Self-Sufficiency Sustainability and Global Food Trade Diversification
(MDPI, 2023) Noora Al-Abdelmalek; Murat Küçükvar; Nuri Cihat Cihat Onat; Enas Fares; Hiba Anis Ayad; Muhammet Enis Bulak; Banu Yetkin Yetkin Ekren; Yigit Kazancoglu; Kadir Ertogral; Kucukvar, Murat; Onat, Nuri C.; Al-Abdelmalek, Noora; Fares, Enas; Bulak, Muhammet Enis; Ertogral, Kadir; Ayad, Hiba
Food trade restrictions pose a serious risk for countries that are heavily reliant on food imports potentially leading to food crises inequality and geopolitical conflicts on a global scale. However such restrictions may also have transformative effects in promoting food supply chain resilience security and self-sufficiency. In this study a novel econometric analysis is presented utilizing a data-driven analytical model to investigate the impact of a food embargo on the industry using Qatar as a case study. A structured and automated food trade database is created using Microsoft Management Server Studio and data visualization software is integrated for automated data discovery. By using a global trade-based sustainability assessment model which combines the multi-region input-output (MRIO) analysis with transportation mode-based (sea road and air) emissions the carbon footprint of the dairy food production sector could be estimated. The study shows that the trade embargo on Qatar’s food industry can lead to significant reductions in the annual import of food products promoting self-sufficiency and reducing the net carbon emissions of the dairy food sector by nearly 40%. This reduction is not only achieved through food supply chain changes such as transportation modes but also by restrictions pushing the country to increase domestic production. Overall the study demonstrates that a trade embargo with the support of a well-designed national food security strategy trade/import diversification and the use of different modes of transportation for food products can improve the resilience of global supply chains self-sufficiency and environmental sustainability. © 2023 Elsevier B.V. All rights reserved.