The aim of this study is to synthesize the studies on the effect of social capital on economic and financial growth with the method of meta-analysis. Social capital has an impact on economic development by influencing individuals, businesses, banks and the way they do business, and thus trade. In this study, the studies carried out in the years 2007-2021 are discussed. In order to be able to interpret the quantitative findings of the studies that have dealt with this subject together, studies made on academic databases were scanned, and the findings consisting of 36 studies meeting the determined criteria and having a total of 3458 observations were synthesized through the meta-analysis method. Research findings of 36 different studies including sample size, standard error, and effect values were analyzed with the help of CMA (Comprehensive Meta-Analysis) program. As a result of the meta-analysis, the effect size was found to be moderate (Fisher Z=0.380). According to the results of the meta-analysis, the H1 was accepted and it was revealed that the social capital level of the countries affected the economic growth at a moderate level. Considering the lack of consensus in the studies in the literature and the difficulty of measuring social capital, it is thought that this general finding is very valuable in terms of the literature and will make an important contribution.

The increasing emphasis on climate change and environmental sustainability worldwide has brought about the convergence and increased focus on financial technologies (FinTech) and green energy initiatives. In light of this, the study’s objective is to investigate how FinTech moderates the relationship between the ecological footprint (EF) and green energy transition (GTE) in the BRICS-T nations between 1990 and 2021. This study examines how fintech moderated the GTE and EF in the BRICS-T from 1990 to 2021. We applied the Fully Modified Ordinary Least Squares (FMOLS) and used Dynamic Ordinary Least Squares (DOLS) as the main estimators for longitudinal analysis. In contrast, Driscoll-Kraay was used to verify the robustness of the results under cross-sectional dependence and heteroskedasticity. The results reveal that FinTech indirectly hinders EF by facilitating GTE. The outcomes also show that FinTech significantly constrains EF, while GDP and industrialization worsen EF. The results also confirm the important role of GTE and foreign direct investment (FDI) in reducing CO2 emissions in BRICS-T countries. Lastly, the paper offers policymakers useful recommendations for lowering EF in light of these outcomes. The study suggests establishing appropriate policies and strategies that encourage FinTech platforms to invest in green energy projects, including financial technology, promoting energy-efficient and low-carbon foreign direct investment, and encouraging GTE.

Particulate matter, particularly PM_2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM_2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health.

Bu çalışmada, 2004-2017 döneminde Türkiye’de faaliyette bulunan 21 bankanın yıllık verileri üzerinden takipteki kredilerin belirleyicileri tespit edilmeye çalışmıştır. Çalışmaya bankaya özgü değişkenlerin yanı sıra ülkeye özgü makro finansal ve makroekonomik değişkenler de eklenmiştir. Çalışmada, takipteki kredilerin toplam aktiflere (NPL1) ve takipteki kredilerin brüt toplam kredilere (NPL2) oranı bağımlı değişken olarak belirlenmiştir. Çalışmada bankaların takipteki alacaklarını etkileyen faktörler statik ve dinamik panel veri analizi yöntemleri ile incelenmiştir. Borç vermede uzmanlaşma (BVU) değişkeni modellerin genelinde negatif ve istatistiki olarak anlamlı bulunmuştur. Buna göre, bankalar kredi vermede uzmanlaştıkça takipteki alacaklarını arttırabilecek kredileri daha iyi takip etme potansiyeline sahip olacaklardır. Kapitalizasyon (KAP) değişkeninin tüm modellerde istatistiki olarak anlamlı ve pozitif etkide bulunduğu tespit edilmiştir. Bu bulgu bankaların düşük sermaye ile daha riskli krediler verdiğini göstermekte olup, bu durum takipteki alacakların artmasına neden olmuştur. Panel veri yöntemi ile yapılan analiz sonuçlarına göre bulduğumuz sonuçlar finansal ve ekonomik faktörler belirlenirken politika yapıcılara yardımcı olacaktır.