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.

This study highlights the importance of managing and analyzing customer reviews to gain a competitive advantage and improve customer experience in the hospitality industry. In this context, a deep learning-based sentiment analysis system of hotel customer reviews is developed to evaluate service quality within the scope of sustainable tourism. The study analyzed 15,522 customer reviews of five-star hotels in Antalya using text mining, topic modelling, and deep learning-based sentiment analysis. The reviews were classified as positive, negative, or neutral. The findings show that Hotel HB2 has the highest performance, with an F1 score of 97.9%. Overall customer satisfaction is 91%, while emotional satisfaction stands at 77%. Key factors, such as cleanliness, food quality, and staff professionalism, were found to play a critical role in customer loyalty. Additionally, this study integrates sustainability-orientated themes by identifying customer feedback related to environmentally friendly practices and sustainable hotel operations. The results provide evidence that customer satisfaction is not only influenced by service quality but also by the perceived environmental and social responsibility of the hotel. Machine learning techniques have emerged as effective tools for analyzing large-scale customer reviews, offering valuable insights to rapidly and accurately capture customers’ emotions, expectations, and perceptions. As a comprehensive application of sentiment analysis and text mining, this research offers hotel managers a practical framework to enhance service quality, foster customer loyalty, and develop sustainability-orientated strategies. This study contributes to the literature by linking AI-driven sentiment analysis with sustainability practices in the tourism sector.

Tourism revenues are a significant source of income under the current account service item of countries. These revenues are not included in exports, despite being compared with the export revenues of the countries and in economics the ratio of tourism revenues to export revenues is used as an indicator. In developing economies, tourism revenues play a role in closing the current account deficit. The prediction of this rate in countries with foreign trade deficit is important in developing tourism, export and import policies for the future. In this study, multiple linear regression method (MLR), one of the traditional methods, and the artificial neural network method (ANN), one of the machine learning methods were used to estimate the rate of tourism revenues of the sample country Turkey to its export revenues. In the model of the study covering 2004-2020 period, the number of tourists received, total income from tourism, average expenditure by tourists per capita, population, total export revenue, growth rate, Euro/TL and US Dollar/TL rates were chosen as independent variables. As a result of the study, the R2 value was found to be 91.7% for ANN and 90.8% for MLR which were very close to the ideal value. According to the predicts made on the model developed based on this, the rate of Turkey's tourism income to total export income in 2025 is estimated as 31.83% according to ANN; 32.73% according to MLR while in 2030, it is estimated to be 33.25% according to ANN and 36.78% according to MLR.

In this study, the estimation of high-tech exports for Turkey’s foreign trade target in line with sustainable development was carried out. The research was carried out for Turkey since it has been focusing on sustainable and environmentally friendly production and an export-oriented growth model, with a transformation in its economic growth strategy as of 2021, and high-tech products are a determining factor in the export target. In this research, three different machine learning techniques, namely artificial neural networks, logistic regression, and support vector regression, were used to determine a successful prediction method close to the ideal scenario. In the models, high technology exports for the period of 2007–2023 with data obtained from the World Bank were taken as the dependent variable, while the gross national product, number of patents, and research and development expenditures were taken as independent variables. By calculating the R², MAPE, and MSE metrics, the success of the model with the least error was evaluated, and it was seen that artificial neural networks (ANNs) were the most successful model, with values of 94.2%, 0.011, and 0.073, respectively. The ANN model was followed by support regression and logistic regression.

Sağlık kuruluşlarının kurumsal itibarı, hastaların sağlık hizmeti alırken güven duymasını ve memnuniyetini doğrudan etkiler. Sağlık kuruluşlarındaki kurumsal itibar, bu kurumların toplum nezdindeki saygınlığını ve imajını belirler. Bu çalışmada Antalya Eğitim ve Araştırma Hastanesine, Google Haritalardan yapılan 1584 yorum üzerinden hastanenin kurumsal itibarı analiz edilmiştir. Çalışmada kurumsal itibarı ölçmek ve belirlemek için metin madenciliği ve duygu analizi yöntemleri kullanılmıştır. Çalışma sonucunda yorumların %54’ü olumlu, %40’ı olumsuz ve %6’sı nötr duygulardan oluşmaktadır. Hasta memnuniyeti, olumlu izlenimlerde doktorlar ve sağlık ekibine duyulan güven ve memnuniyetin vurgulanmasıyla birlikte, hizmet kalitesi ve sunulan imkanlardan memnuniyet ifadesiyle kendini göstermektedir. Olumsuz izlenimlerde hastalar personel tutumu, bekleme süreleri, hizmet kalitesi, hijyen ve yoğunluk gibi konularda memnuniyetsizlik yaşadıklarını belirtiyorlar. Nötr izlenimlerde ise hastaların genellikle hastane ve personel hakkında tarafsız değerlendirmeler yaptıkları, hizmetler ve süreçler hakkında nesnel bir dil kullandıkları ortaya çıkmıştır. Bu çalışma sağlık kuruluşlarının iyi oldukları ve geliştirmeleri gerektiği konular hakkında fikir vermektedir.

Green finance has become a key component of sustainability transitions by supporting the alignment of financial systems with environmental and climate-related objectives. As discussions on sustainable investments and climate finance increasingly take place in digital environments, analyzing large-scale user-generated content has become important for understanding online discussions and emerging discourse patterns. This study investigates green finance discourse in the digital public sphere using a multi-method Natural Language Processing (NLP) framework. A dataset of 23,452 posts collected from the platform X was analyzed using TF-IDF-based word frequency analysis, FinBERT-based sentiment analysis, Latent Dirichlet Allocation (LDA) topic modelling, and keyword co-occurrence network analysis. The results indicate that green finance discourse is dominated by positive (44.61%) and neutral (45.85%) sentiment, suggesting a generally favorable and institutionalized public perception. Topic modelling identified three dominant themes: climate finance and sustainability transition, digital finance and speculative investment narratives, energy investments and financial infrastructure. Network analysis revealed that sustainability, climate, energy, investment, and finance constitute the core conceptual structure of discourse. These findings demonstrate the effectiveness of NLP-based approaches for analyzing large-scale digital discussions and provide insights for policymakers, financial institutions, and organizations seeking to better understand online discussions surrounding green finance and support more effective sustainability communication strategies.