Browsing by Author "Onat, Nuri C."

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Carbon Footprint of Food Production: A Systematic Review and Meta-Analysis
(Nature Portfolio, 2025-10-13) 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: 3
Citation - Scopus: 3
Carbon Footprint of Food Production: A Systematic Review and Meta-Analysis
(Nature Portfolio, 2025-10-13) 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, 2023-03-25) 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.