Climate neutrality is the main environmental goal set for the European Union Member States until 2050. EU economies can achieve this ambitious climate goal by reducing the emission intensity of economies, which has been achieved for many years by reducing pollution emitted by industry. The aim of the study is focused primarily on demonstrating the degree of relationship between the variables describing economic growth, GDP, and the level of CO₂ emissions. In the first stage of the research, the potential of countries to achieve climate neutrality was assessed, which was achieved by estimating the correlation between GDP indices in relation to 2013 and the level of CO₂ emissions. Research has shown that despite the countries’ differences in the structure of their energy balances, they can achieve independence of economic growth from the emission level of their economies. The research also concerns Poland’s special situation compared to other European Union countries according to energy balance based on coal. A model based on differential equations was used to simulate the impact of GDP, energy intensity, and the share of biofuels on temperature and CO₂ concentration until 2030, using data for Poland as an example. The aim of this analysis is to answer the question of whether the energy transformation in the country will achieve the assumed emission reduction goals by 2030.

With the development of Artificial Intelligence (AI) technology, more and more AI chatbots (e.g., ChatGPT and DeepSeek) are beginning to affect work and lifestyles. Although AI chatbots have brought many opportunities to education and teacher trainees, they have also caused many problems and resistance among some teacher trainees. However, previous studies have focused more on the influence of positive acceptance factors induced by AI chatbots and less on the negative barrier model induced by AI chatbots. Therefore, this study starts from the negative barrier factors induced by AI chatbots and builds an influencing barrier model of AI chatbot resistance guided by Innovation Resistance Theory (IRT) and appropriately draws on Cultural Dimension Theory (CDT), Unified Theory of Acceptance and Use of Technology (UTAUT), and practical characteristics. The questionnaires mainly adopt convenience sampling and snowball sampling methods, and the data are empirically analyzed. The results show that Uncertainty Avoidance, the Social Influence Barrier, and Technology Anxiety have a significant and direct influence on teacher trainees’ resistance to AI chatbots. Meanwhile, Uncertainty Avoidance, the Social Influence Barrier, and Technology Anxiety play significant mediating roles in the impact of the Usage Barrier (UB), Image Barrier (IB), Value Barrier (VB), Risk Barrier (RB), and Tradition Barrier (TB) on resistance behaviors, revealing the complex path through which cognition-emotion-society factors jointly shape technology resistance behaviors. Therefore, this study not only contributes to enriching the theoretical results of combining Innovation Resistance Theory with AI chatbots and adding new research paths (e.g., the mediating role of Uncertainty Avoidance) but also provides a practical guide for the dissemination of AI chatbots among teacher trainees and future technological talents in a sustainable future.

As the European Union’s energy systems are transforming towards achieving climate goals, this article examines the energy balances of EU member states. This analysis covers, among other things, the dynamics of energy dependence and strategies for decoupling economic growth from the level of emissions in the European Union (EU), with particular emphasis on Poland, which is strongly influenced by its historical reliance on coal in the energy balance. Using panel data from 1990 to 2022, the article investigates differences in energy dependence between individual countries, shaped by economic structures and national energy policies. The study results confirm significant heterogeneity between member states and emphasize that the stability and direction of decoupling economic growth from greenhouse gas (GHG) emissions are strongly dependent on the composition of the energy mix and vulnerability to external conditions. Based on scenario analysis, potential paths for Poland’s energy transition are assessed. We demonstrate that a high share of renewable energy sources (RES) significantly reduces CO₂ emissions, provided it is accompanied by infrastructure modernization and the development of energy storage. Furthermore, integrating nuclear energy as a stabilizing element of the energy mix offers an additional path to deep decarbonization while ensuring supply reliability. Finally, we demonstrate that improving energy efficiency and demand management can effectively increase energy security and reduce emissions, even in a scenario with a stable coal share. The study addresses a research gap by integrating decoupling analysis with scenario-based stochastic modeling for Poland, a country for which few comprehensive transition assessments exist. The results provide practical guidance for developing resilient, low-emission energy policies in Poland and the EU. Results are reported for 2025–2050 (with 2040 as an interim milestone).

The purpose of the article is to characterize crisis management, including the main stages of the activities of anti-crisis headquarters. The paper presents extensive examples of critical infrastructure and develops sample maps in QGIS software, which can be important tools in conducting the activities of crisis management services. It presents A Free and OpenSource Geographic Information System QGIS software for identifying selected critical infrastructure objects based on available GIS Open data from the Malaysian region and Poland. The analysis presents selected geoprocessing tools for generating areas with a set distance from identified critical infrastructure objects called buffers. Buffer layers are areas, visible on the generated maps, that can be used as a tool to visualize potential actions for emergency management services. Identifying these buffer zones makes it possible to build strategies for implementing adequate prevention or rescue actions during an emergency. Risk classification in specific buffer zones is presented, which can be used to optimize the actions taken by crisis management services. A wide range of functionality of geographic spatial information systems is demonstrated, which increases the efficiency of operations and optimization of decision-making in crisis management. The publication can provide a valuable example of the use of available information systems in crisis management.