This paper investigates the spatial and social dimensions of light pollution through the lens of electricity consumption and technical losses related to public lighting across Hungarian municipalities, focusing on the Western Transdanubia region. Unlike traditional approaches based on remote sensing, this paper employs local-level electricity use data as a proxy for assessing light pollution intensity. Notably, several sparsely populated rural settlements display disproportionately high per capita electricity use and losses, suggesting inefficiencies, potential over-lighting, and elevated exposure to light pollution. The inclusion of technical losses provides a more accurate picture of systemic inefficiency that may contribute to excessive artificial illumination without corresponding social benefit. These findings underscore the relevance of electricity data as an indirect but actionable source for evaluating light pollution and energy waste. The paper advocates for more data-driven, environmentally conscious, and socially equitable lighting policies that integrate energy efficiency and light pollution mitigation into local planning frameworks. Based on data from 657 municipalities, total public lighting consumption ranges from under 10 MWh in small villages to over 1,000 MWh in large cities, with a regional average of 51.3 MWh (SD: 338.3 MWh). Per capita consumption varies between 0.002 and 0.02 MWh/person, with technical losses up to 17.9 kWh/person in some rural areas. These results indicate that energy inefficiency and light pollution are often more pronounced in small settlements, highlighting the need for regionally tailored modernization strategies addressing both energy use and environmental impact.

Urban lighting systems contribute significantly to energy consumption and light pollution, raising environmental and societal concerns. This paper explores the potential of Artificial Intelligence (abbreviation: AI)-driven adaptive urban lighting as a sustainable solution, framed within a multi-level perspective on socio-technical transitions. At the landscape level, increasing urbanization and global sustainability targets exert pressure for energy-efficient practices, while traditional street lighting regimes remain largely rigid and resource-intensive. At the niche level, we propose a novel adaptive lighting system integrating real-time Internet of Things (abbreviation: IoT) sensor data and machine learning algorithms to dynamically adjust illumination based on traffic, pedestrian activity, weather conditions, and ambient light. Studies demonstrate that the proposed approach can significantly reduce energy use while minimizing light pollution, without compromising safety or visibility. The results indicate that such niche innovations, supported by AI and renewable energy integration, have the potential to influence broader regime change and contribute to sustainable urban development. This research highlights the importance of combining technological innovation with socio-technical frameworks to address pressing urban environmental challenges, offering insights for policymakers, urban planners, and energy managers seeking to balance efficiency, safety, and ecological impact.

The popularity of asbestos-containing products stemmed from their fire resistance, thermal insulation properties, and mechanical strength. However, their well-documented adverse health effects led to the prohibition of their use in many countries. This research aims to conduct a comprehensive examination of the often-overlooked social dimensions associated with asbestos, with a specific focus on the affected population’s circumstances and the potential solutions accessible to them. Its analysis encompasses legal regulations concerning asbestos, societal awareness, and the economic implications of asbestos removal from the perspective of those impacted. The findings highlight that the remediation of asbestos-containing products is often contingent on the financial and social conditions of the affected population, posing significant challenges for the economic sector and environmental protection efforts. This research contributes to the development of integrated approaches that address social, economic, and environmental dimensions in tandem. Its originality lies in situating the concepts of social sustainability and socially oriented environmental development within the context of asbestos-related policies. The findings suggest that achieving asbestos-free environments is feasible only through the integration of social dimensions, taking into account the economic and social conditions of the affected communities.

Urban sustainability has become a central challenge for cities, requiring rigorous frameworks to measure environmental performance and support evidence-based policy. Indicator-based benchmarking systems enable comparison and learning. At the European Union level, and specifically at the municipal level, these are most commonly represented by the European Green Leaf Award (EGLA) and the European Green Capital Award (EGCA), which play an increasing role in shaping urban sustainability agendas. The paper examines environmental characteristics of the 18 EGLA-winning cities between 2015 and 2026 and is guided by two research questions: (1) What patterns of environmental characteristics can be identified among EGLA-winning cities? (2) Do these cities converge toward a common environmental model, or do they exhibit multiple sustainability pathways? The study addresses these questions by using a multivariate, data-driven approach: environmental indicators across key domains are examined through Multiple Factor Analysis (MFA) and k-means clustering to identify patterns of similarity. The results indicate substantial differences in sustainability performance and heterogeneity: EGLA-winning cities do not converge toward a single model but exhibit distinct environmental profiles reflecting different policy priorities and local conditions. The results may provide information for municipalities on the green transition and environmentally friendly developments, especially in small and medium-sized cities.