Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2026Scopus SJRQ2The Relationship Between Agricultural Carbon Dioxide Emission and Agriculture Subsectors Production: Static Panel Data Approach
Peker, Ayşe Esra; Akdağ, Nuran; Veysikarani, Dilek; Kaya, Mustafa Göktuğ; Kaya, Perihan Hazel
Human activities contribute to carbon dioxide (CO₂) emissions, which have serious environmental, economic, and social effects of climate change. This has sparked efforts to find large-scale solutions to eliminate the causes of climate change. While these solutions mainly involve reducing carbon emissions through global agreements, individual nations have also taken steps to cut emissions by changing production methods, such as irrigation and fertilisation, to ensure the efficient and effective use of resources and the proper storage and transportation of food. As a result, the agricultural sector is both a source and a victim of this process. The sector’s environmental impact varies due to differing production techniques and energy needs across various sub-sectors like vegetable, grain, and fruit production. Increases in carbon dioxide emissions in this sector are connected not only to production activities but also to demographic and social factors, such as the rural population proportion. This study aims to explore the effect of vegetable, grain, and fruit production, as well as the rural population ratio, on carbon dioxide emissions. To achieve this, a static panel data analysis was conducted across 21 European countries from 1970 to 2023. According to the research findings, vegetable production increases carbon emissions from agriculture. However, it was found to have a significant, albeit borderline, impact on fruit production and to reduce carbon emissions. These results demonstrate that agriculture's contribution to carbon dioxide emissions varies by sub-sector and provide valuable information for developing sustainable agricultural policies.
- MakaleYalnız künye2026Scopus SJRQ1Analysis of the impact of the social justice index on the COVID-19 pandemic using artificial intelligence in EU and OECD countries
Utku, Anıl; Can, Ümit; BARUT, Abdulkadir; Pilatin, Abdulmuttalip; Sharabati, Abdel-Aziz Ahmad
The COVID-19 disease has evolved into a pandemic that affects the world in multiple dimensions. While COVID-19 spread rapidly to several nations, distinct patterns of spread emerged in each nation. This spreading behavior is related to various characteristics of countries, including social, political, and economic factors. In this study, an analysis of the COVID-19 pandemic in EU and OECD countries was conducted, taking into account the social justice index for the first time. The impact and significance of the social justice index on the COVID-19 analysis results were attempted to be revealed. As a result, it is understood that the countries with high Social Justice Index scores and sub-dimension scores are more successful in combating the COVID-19 pandemic. In addition, a new LSTM-based model was developed to predict the number of cases and deaths in these countries. The designed LSTM approach was compared with some well-known deep learning and machine learning algorithms. As a result of the experimental studies, the LSTM model obtained the best prediction results. Finally, the similarities between these countries in terms of the characteristics of the COVID-19 pandemic, including death and case spread, were analyzed, and exciting results were disclosed.
