Climate awareness caused by the threat of global warming is the number one agenda item for developed and developing economies. Plans developed in this context, environmentally friendly trends in economic activities, and countries’ efforts to adapt to sustainable development have enabled new road maps. The most important of these efforts is the Paris Climate Agreement signed in 2015 and the Green Deal implemented by the European Union (EU) within the framework of this agreement. In this study, the carbon emissions of Turkey, which has important trade relations with the EU, were estimated using machine learning techniques, and a prediction was made for 2030 based on the results obtained. These results were evaluated in line with the targets of the Green Deal. The R² of support vector regression (SVR), which was applied in the model as one of the machine learning techniques, was found to be 98.4%, and it was found to have the highest predictive power. This technique was followed by multiple linear regression (MLR) with a 97.6% success rate and artificial neural networks (ANN) with a 95.8% success rate, respectively. According to the estimates achieved with the most successful model, SVR, Turkey’s carbon emissions are expected to be 723.97 million metric tons (mt) of carbon dioxide (CO₂) in 2030, the target year set by the EU. This level is 42% higher than the target that needs to be achieved given the data existing in 2019. According to these results, Turkey will not be able to reach the targets set by the EU unless it increases its coal-based energy consumption and provides incentives for renewable energy sources.

Son yıllarda dijital platformların yaygınlaşmasıyla birlikte, otel müşterilerinin deneyimlerini çevrim içi olarak paylaşmaları hem potansiyel tüketiciler hem de otel yöneticileri için önemli bir veri kaynağı haline gelmiştir. Bu çalışmada, Kuzey Kıbrıs Türk Cumhuriyeti’nde faaliyet gösteren ve otelpuan.com platformunda en çok yorum alan beş otel, toplam 3.000 müşteri yorumu üzerinden analiz edilmiştir. Her otel için son 600 yorum alınarak oluşturulan veri seti, metin madenciliği, duygu analizi, kelime frekansı, kelime bulutu ve konu modelleme (LDA) yöntemleriyle değerlendirilmiştir. Duygu analizinde TextBlob kütüphanesi kullanılmış, yorumlar olumlu, olumsuz ve nötr olarak sınıflandırılmıştır. Ardından yapılan konu modelleme ile her otel için üç ana tema belirlenmiş ve bu temalar üzerinden müşteri memnuniyetine dair detaylı bulgular elde edilmiştir. Çalışma ayrıca kurumsal itibar çerçevesinde, müşteri yorumlarının otellerin dijital itibarına nasıl yansıdığını ortaya koymuştur.

In the contemporary era, the escalating consumption phenomenon has prompted concerns about resource depletion and sustainability. As consumers attain a certain level of satisfaction within the capitalist system, their awareness of sustainability grows, leading to a shift towards conscious consumption. This transition accentuates the importance of electronic word of mouth (eWOM), where regular consumers share their experiences, countering traditional capitalist practices. This study aims to conduct a descriptive-quantitative analysis of academic research on eWOM, spanning from the emergence of digital marketing to the present. A comprehensive examination of 1642 published articles indexed in the Web of Science (WoS) database between 2003 and 2023 was performed using bibliometric methods. The analysis reveals a remarkable surge in eWOM publications since 2009. Notably, "Flieri R" emerges as the most prolific author in the field, having contributed 17 academic studies. Among the journals, "Sustainability" stands out with the highest number of articles, totaling 63. Furthermore, recent years have witnessed a clustering of related concepts, such as "sustainability," "service quality," and "digital marketing," around the eWOM domain.