Air pollution remains a major public health concern globally, especially in the Western Balkan countries facing severe air quality problems. This study investigates the relationship between air quality, news media sentiment, and public discourse in Macedonia over a ten-year period (2014–2023). We employed sentiment analysis to examine the emotional tone of news coverage related to air pollution, and topic modeling to uncover recurring themes within news articles. Our analysis revealed a distinct seasonal pattern, with negative media sentiments peaking during winter months when PM10 levels were the highest. This finding aligns with the increased reliance on polluting fuels for winter heating. Interestingly, despite a stable number of neutral articles, a rise in positive-sentiment articles suggests a potential decrease in pollution levels or the effectiveness of new government policies. We identified recurring topics like air quality concerns in specific cities, public unease regarding factories, and ongoing scrutiny of government policies. Emerging topics included the impact of the COVID-19 pandemic on air quality, public discourse surrounding heating practices, and growing concerns about waste management. This study contributes to a deeper understanding of the complex interplay between air pollution data, public discourse, and media framing, offering valuable insights for policymakers and media outlets in Macedonia.

Reducing the energy consumption growth rate is increasingly becoming one of the main challenges for ensuring sustainable development, particularly in the buildings as the largest end-use sector in many countries. Along this line, the aim of this paper is to analyse the possibilities for energy savings in the construction of new buildings and reconstruction of the existing ones developing a tool that, in terms of the available heating technologies and insulation, provides answer to the problem of optimal cost effective energy consumption. The tool is composed of an unsteady heat transfer model which is incorporated into a cost-effective energy saving optimization. The unsteady heat transfer model uses annual hourly meteorological data, chosen as typical for the last ten-year period, as well as thermo physical features of the layers of the building walls. The model is tested for the typical conditions in the city of Skopje, Macedonia. The results show that the most cost effective heating technology for the given conditions is the wood fired stove, followed by the inverter air-conditioner. The centralized district heating and the pellet fired stoves are the next options. The least cost effective option is the panel that uses electricity. In this paper, the optimal insulation thickness is presented for each type of heating technology.

Understanding how societies perceive and adapt to environmental change requires analyzing awareness as a dynamic process emerging from coupled human–environment interactions. This study presents a longitudinal, data-driven analysis of environmental awareness based on three years of social-media discourse related to air pollution. Using sentiment analysis (Valence Aware Dictionary and Sentiment Reasoner and Twitter-RoBERTa) and topic modeling (BERTopic), we quantify the temporal evolution of collective public awareness and identify dominant discourse themes. A correlation analysis between sentiment indicators and PM10 levels reveals synchronized fluctuations, while mutual information indicates that public awareness becomes increasingly dependent on or shaped by pollution variability. Furthermore, Kruskal–Wallis tests confirm statistically significant temporal variations in sentiment distributions, underscoring adaptive shifts in public awareness across years. By interpreting awareness as a measurable order parameter within a complex socio-environmental system, this work demonstrates how collective perception emerges from nonlinear feedbacks between environmental forcing and public information flow. The proposed multilayer socio-environmental perception framework integrates machine-learning-based sentiment and topic analysis with complex-systems concepts to quantify emergent awareness dynamics from longitudinal social-media data.