Mohammad Jaber

Rapid conversion of agricultural land into low-density housing reshapes local water balances at a time when climate change is intensifying hydrological extremes in Central Europe. This study evaluates the rooftop rainwater harvesting (RWH) potential of a newly urbanized housing estate in Rokietnica (Greater Poland Voivodeship, Poland) using a publicly available true orthophoto (5 cm ground sampling distance) as a reliable geometric data source. Roof footprints of 41 single-family buildings (5305 m² in total) were manually vectorized in QGIS and combined with monthly precipitation recorded at the nearest meteorological station (Złotniki) in a simplified volumetric model (V = A × R × C, with C = 0.95). To place the single reference year (2021) in its climatic context, the full 72-year precipitation record (1952–2023) was analyzed using the Mann–Kendall test and Sen’s slope estimator. The results indicate a harvestable volume of approximately 2642 m³ in 2021 (53–73 m³ per building; mean 64 m³), with May and August jointly accounting for almost one-third of the annual total. Annual precipitation at Złotniki exhibits a statistically significant increasing trend (+1.29 mm yr⁻¹; p = 0.028) concentrated in winter and early spring, while summer totals remain trendless but highly variable. A monthly storage simulation driven by the recent 30-year record (1994–2023) shows that summer garden irrigation, the dominant practical application of harvested rainwater in Polish households, is considerably harder to meet from the roof alone than year-round indoor non-potable uses: a 5 m³ tank covers approximately 73% of the seasonal demand of a 100 m² garden plot, while a 3–5 m³ tank would theoretically secure 91–98% of toilet-flushing demand for a four-person household. Under Poland’s national rainwater co-financing scheme (formerly “Moja Woda,” active 2020–2024, succeeded by “Mikroretencja” from June 2026), simple payback periods of roughly 3–7 years make household installations financially defensible. The findings suggest that true orthophotos enable rapid, low-cost RWH assessments in dynamically developing suburbs, and that storage sizing should anticipate the ongoing seasonal redistribution of precipitation under climate change. To our knowledge, this is the first study to combine free national true-orthophoto imagery, a 72-year Mann–Kendall trend analysis, and a subsidy-linked storage-reliability simulation within a single suburban rainwater-harvesting assessment.

This study models the dynamics of digital skills in Hungary and the European Union using discrete-state Markov chains applied to Digital Economy and Society Index (DESI) indicators from 2017 to 2022. Empirical transition matrices are estimated for four DESI-relevant indicators: internet use and basic digital skills, ICT specialist employment, ICT graduate production, and enterprises providing ICT training. Limiting distributions are computed to identify long-run equilibria and divergence from the EU average. The findings reveal distinct patterns in enterprise training and specialist development, highlighting areas where policy interventions may yield the greatest long-term benefits. By explicitly linking dynamic DESI indicators to the United Nations Sustainable Development Goals (SDGs), this research demonstrates how Markov-based transition and steady-state measures can inform progress toward SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 10 (Reduced Inequalities). Policy recommendations leverage transition probabilities to target enterprise incentives, lifelong learning, and regional upskilling, thereby accelerating equitable digital outcomes. Bootstrap confidence intervals indicate that most steady-state estimates carry substantial uncertainty given the short estimation window (six annual observations per indicator–country dyad); results should therefore be interpreted as indicative of directional tendencies rather than as definitive long-run predictions.

While total solar PV capacity in Hungary was only 1 MW in 2010, this figure had grown to 7551 MW by 2024 as a result of the favorable settlement system, subsidies, and the uncertainty caused by the Russo-Ukrainian war. At that time, more than 6.4% of households were already prosumers. In our study, we focus on Hungarian districts, examining the spatial patterns of household-scale solar PV systems and the main drivers of technology adoption in 2024. We use the Theil T index to examine spatial heterogeneity and the Moran’s I statistic to test for spatial dependence. Spatial autocorrelation is further explored using maps based on Local Moran’s I and Local Geary statistics. Finally, a spatial error model is applied to identify the factors influencing the share of household-scale solar PV systems per 100 households. Our results show that the spatial error variable has the largest effect, with household education, the age and size of the building stock, population growth, and built-up area also having significant effects. This confirms the need for a spatially sensitive policy approach and for incorporating space and spatial relations in energy economic studies.

While total solar PV capacity in Hungary was only 1 MW in 2010, this figure has grown to 7,551 MW by 2024 as a result of the favorable settlement system, available subsidies, and uncertainty caused by the Russian-Ukrainian war. More than 6.4% of households are already prosumers. In our study, we focus on Hungarian districts, examining the spatial patterns of household-scale solar PV systems and the main drivers of technology adoption in 2024. We use the Theil T index to examine spatial heterogeneity and the Moran’s I statistic to test for spatial dependence. Spatial autocorrelation is further explored using maps based on the Local Moran’s I and Local Geary statistics. Finally, a spatial error model is applied to identify the factors influencing the share of household-scale solar PV systems per 100 households. Our results show that the spatial error variable has the largest effect, complemented by household education, the age and size of the building stock, population growth, and the built-up area. It confirms the need for a spatially sensitive policy approach and the importance of space and spatial relations in energy economic studies.

This paper examines the main characteristics of people living in income poverty in Borsod-Abaúj-Zemplén County, one of Hungary’s most disadvantaged regions. Utilizing a questionnaire-based survey conducted between November 2024 and February 2025, the study analyzes the relationship between poverty status and key sociodemographic variables, including education, ethnicity, household size, settlement type, and distance from the county seat. Results indicate that lower educational attainment and Roma ethnicity are significantly associated with higher poverty rates, while rural residence and greater distance from the county seat also increase vulnerability to poverty. In contrast, household size does not show a statistically significant effect. The findings highlight persistent spatial and social inequalities in the region, emphasizing the need for targeted policy interventions focusing on education, ethnic integration, and regional development.

This study examines the interrelated challenges of climate change and energy poverty across two distinct industrial regions: Borsod-Abaúj-Zemplén in Hungary and Zarqa in Jordan. Both areas face unemployment and low-income levels, as well as environmental legacies of industrial activity; however, they differ significantly in their energy policies and infrastructure development. Using 2025 survey data, we develop indices of energy poverty, financial poverty, and climate perceptions, aligned with OECD guidelines. Regression analysis indicates that the model accounts for approximately 40% of the variance in energy poverty. Notably, heightened perceptions of climate change are associated with increased reports of energy hardship, suggesting that economically deprived households possess greater climate risk awareness. Resilience capacities, including adaptive skills, income stability, and community support, are found to substantially mitigate energy poverty. Income and employment status also play protective roles, underscoring the importance of economic resources. The impact of financial poverty varies markedly, being negligible in Hungary but severe in Jordan due to structural and infrastructural constraints. Our findings underscore the need for tailored, inclusive policy interventions that emphasize energy efficiency and retrofitting in Hungary and promote financial support and the adoption of renewable energy in Jordan. Integrating principles of energy justice into climate resilience strategies is crucial for promoting equitable and sustainable energy transitions, mitigating local vulnerabilities, and enhancing overall household resilience.

This study examines the factors influencing energy-saving behaviors in Jordanian households by expanding the Theory of Planned Behavior (TPB). Incorporating additional variables, such as energy poverty, eco-literacy, and environmental concern, this study utilizes survey data collected through online tools. The findings reveal that environmental concern and eco-literacy significantly influence attitudes, perceived behavioral control, and subjective norms, with attitudes emerging as the dominant predictor of environmentally sustainable intentions. These intentions, in turn, positively influence actual sustainable behaviors. Energy poverty directly affects both intentions and behaviors, moderating key relationships within the model, notably diminishing the influence of attitudes on intentions and the translation of intentions into behavior. The results validate the applicability of TPB to sustainable energy practices and demonstrate the importance of addressing economic barriers. Overall, the findings suggest that fostering environmental concern and eco-literacy is necessary but insufficient without interventions that mitigate financial constraints associated with energy poverty.

Energy poverty, resulting from poor energy efficiency and economic and social barriers to accessing appropriate, modern, and sustainable energy services, remains a critical issue in Jordan, a country facing growing climate pressures, particularly given its history of rapid urbanization. This study examines energy poverty through a multidimensional lens, considering its spatial and socio-demographic variations across Jordan. Drawing on data from 19,475 households, we apply a novel energy poverty index and binary logistic regression to analyze key determinants of energy poverty and discuss their intersection with climate vulnerability. The energy poverty index (EPI) is structured around four pillars: housing, fuel, cooling, and wealth. The results show that 51% of households in Jordan are affected by energy poverty. Contributing factors include geographic location, gender, age, education level, dwelling type, ownership of cooling appliances, and financial stability. The results indicate that energy poverty is both a socio-economic and infrastructural issue, with the highest concentrations in the northern and southern regions of the country, areas also vulnerable to climate risks such as drought and extreme heat. Our findings emphasize the need for integrated policy approaches that simultaneously address income inequality, infrastructure deficits, and environmental stressors. Targeted strategies are needed to align social and climate policies for effective energy poverty mitigation and climate resilience planning in Jordan.