This study investigates the impact of climate anxiety on young adults’ consumer and social behaviour. Data were collected via a questionnaire survey among 696 university students from Széchenyi István University, Budapest Metropolitan University, and Neumann János University. The survey focused on various aspects of climate anxiety, including its frequency, intensity, perceived life impact, emotional responses, and management strategies. The analysis, supported by AI tools, identified two distinct clusters: one with moderate anxiety levels and a strong interest in learning about climate change, and another with higher anxiety levels but less desire for further information. Various statistical models, including Naive Bayes, logistic regression, and random forests, were employed to identify behavioural patterns, with decision trees showing the lowest classification error. The study highlights the significant influence of climate anxiety on the shift towards sustainable consumption and active engagement in climate action. Recommendations for future research include the broader application of deep learning models and extending the study to other demographic groups. Longitudinal data collection is also suggested to track long-term trends and inform effective public policy and communication strategies. The findings emphasise the need for comprehensive approaches to understanding and addressing climate anxiety’s societal impacts.

The impact of climate change is increasingly evident in various domains today and is gaining prominence in scientific inquiries. Climate change also affects the utilisation of renewable energies. The article examines the effects of 21st-century climate change on the annual electric energy production of medium-sized photovoltaic module systems. The study bases its analysis on three possible scenarios: a pessimistic (RCP 8.5), a less pessimistic (RCP 4.5), and an optimistic (RCP 2.6) scenario. The applied Representative Concentration Pathways (RCP) scenarios were developed by the Intergovernmental Panel on Climate Change (IPCC) to enhance comparability in analyses related to climate change. Compared to older linear models, an innovation utilises a more flexible and multidirectional model. One of the article’s key findings is that, for all three examined settlements, the annual yield of the studied photovoltaic systems will surpass the levels of the base year 2010 by the middle and end of the century. Another significant outcome is that, under the three scenarios analysed, the ratio of annual performance variation to annual global radiation variation shows substantial differences by the middle and end of the century compared to the 2010 baseline. In the optimistic scenario, this ratio exceeds 1, whereas in the pessimistic and less pessimistic scenarios, it falls below 1. This ratio does not directly inform about the annual production—which increases in all cases—but rather about the changes in efficiency. These efficiency changes are influenced by the rise in annual average temperatures and the fluctuation in sunny hours yearly. The third finding reveals that under the climate change pessimistic scenario (RCP 8.5), the efficiency decrease is less adverse than in the less pessimistic scenario (RCP 4.5).

Electricity constitutes a significant part of the consumption basket of European households and companies. Since energy products are essential components of almost all products and services, any change in energy prices directly impacts the general price level of those products and services. Therefore, this study aims to conduct a comprehensive analysis of power exchange data between 2019 and 2022. For the analysis, we examined the data of 15 countries. In the research, we compared electricity prices in European power exchanges using the Jaccard similarity index and the overlap coefficient, using the DAM hourly prices between 1 January 2019 and 31 December 2022. We transformed the time series into networks using the visibility graph procedure and compared the networks of the studied countries using the two comparison methods with the degree distribution functions. Our aim is to examine how the market anomalies caused by the COVID-19 pandemic and the Russian–Ukrainian conflict affect European electricity markets and how quickly the repercussions spread across the studied countries’ exchanges, and whether they show persistent or anti-persistent characteristics. The results support that similar market effects significantly influence the pattern of price changes among the countries. The methods forming the basis of the research can provide significant assistance in analyzing market trends and contribute to a better understanding of market processes.