This study aims to design an evidence-based policy (EBP) model for household waste management at the village level, emphasizing the importance of a contextual and collaborative approach. The central research question is: How can a household waste management policy model be developed to effectively respond to local dynamics, citizen preferences, and institutional capacity? This research combines quantitative and qualitative approaches through surveys, observations, in-depth interviews, and focus group discussions (FGDs) in eight sub-districts in Bandar Lampung City. The evaluation was conducted using the analytical hierarchy process (AHP) framework and actor mapping with an influence-interest grid to identify the relationship structure and stakeholder contributions.The findings of this study indicate that a policy scenario combining regulations, incentives, and education (scenario C) is the most effective and sustainable alternative in aggregate. However, citizen preferences and institutional capacity across urban villages continue to vary, necessitating adaptive and contextual policy design. The city government (Environmental Agency) remains a key actor, while waste banks, local communities, and neighborhood associations play strategic roles in strengthening institutional social capacity. The proposed policy model emphasizes the integration of micro (citizens and communities) and macro (regulations and institutions) dimensions, and encourages inclusive, adaptive and evidence-based local socio-ecological transformation. This study emphasizes the importance of waste management policies that are evidence-based, collaborative among actors, and flexible to the socio-ecological context. The EBP model developed is relevant for replication in medium-sized cities in the Global South with similar challenges. However, effective replication requires a bottom-up learning approach, namely learning from residents’ narratives, micro-observations, and community-based experimentation, rather than simply copying policies.
Methane (CH$_4$) emissions from the oil and gas industry account for a significant portion of greenhouse gas (GHG) emissions and contribute to global warming. The objective of this research is to estimate and describe the size, pattern, and determinants of CH$_4$ emissions, with a focus on areas where gas flaring is prevalent. By merging satellite emission information with energy production levels and environmental policy makers, the study provides an empirical examination of the interaction between regulation of flaring, and CH$_4$ leakages. The study employs a panel data econometric model to identify the primary drivers of emissions in oil-producing basins. Results indicate that weak enforcement of regulations and the flaring ratio are strongly associated with high CH$_4$ emissions. The findings provide valuable insights for planning targeted mitigation an action, enhancing regulatory compliance, and supporting the transition to clean energy systems.
Construction projects frequently encounter field constraints that affect cost, schedule, and quality performance. When delays arise, contractors often adopt overtime work as an acceleration strategy using the existing workforce. However, such practices may lead to concerns regarding productivity decline. This study investigates the impact of overtime work on construction labor productivity based on the Five-Minute Rating method, focusing on plastering and skim coating activities in a residential project in Palangka Raya, Indonesia. A systematic work sampling approach was employed, comprising 1,296 observations collected over six days, with comparisons made between regular working hours and overtime periods. The results indicate distinct productivity responses across work types. Plastering exhibited only a marginal reduction in Labor Utilization Rate (LUR) of approximately 1%, whereas skim coating showed a more pronounced decline of about 6.5% during overtime. Effective activities decreased by approximately 6% under overtime conditions. In contrast, volume-based analysis suggests that output increased during overtime, with gains of 28% for plastering and 49% for skim coating. Statistical analysis suggested a significant difference in productivity for skim coating (p = 0.031), while no statistically meaningful difference was observed for plastering (p = 0.109) at the 95% confidence level. Despite the observed increase in output, the achieved productivity levels remain below standard unit price analysis benchmarks.
This study provides a comprehensive assessment of long-term climate variability in Palembang, Indonesia, over the period 1992–2025, with particular emphasis on temperature-driven heat exposure and associated environmental health risks. Monthly observational data obtained from the Meteorology, Climatology, and Geophysics Agency (Badan Meteorologi, Klimatologi, dan Geofisika, BMKG) were analyzed to evaluate trends in air temperature, relative humidity, precipitation, and wind speed. Linear regression and anomaly-based approaches were applied to quantify temporal changes relative to a 1992–2025 climatological baseline. The results reveal a pronounced and sustained warming trend, with mean air temperature increasing by approximately 1.3–1.5 ℃ and peak anomalies exceeding +2.0 ℃ in recent years. The frequency of extreme heat months ($\geq$90th percentile) has increased substantially since 2010. In contrast, relative humidity remains persistently high ($\geq$80%) with limited long-term variation, while rainfall and wind speed exhibit strong interannual variability associated with El Niño–Southern Oscillation (ENSO) dynamics. These findings indicate intensifying thermal stress and increasing environmental health risks, underscoring the need for integrated climate–health adaptation strategies, including early warning systems and urban resilience planning in rapidly urbanizing tropical regions.
Rapid urbanization and land-use transformation have intensified thermal stress in mid-sized cities of Bangladesh; however, spatially explicit environmental screening of heat-related risk remains limited. This study investigates the spatiotemporal dynamics of urban heat risk in Kushtia District from 2010 to 2024 using an environmentally weighted, indicator-based geospatial framework integrating remote sensing and demographic data. Multi-temporal Landsat (Thematic Mapper (TM); Operational Land Imager (OLI); OLI/Thermal Infrared (TIRS)) and WorldPop datasets were employed to derive five environmental indices: Land Surface Temperature (LST), Albedo, Urban Thermal Field Variance Index (UTFVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), along with Population Density as a proxy indicator of human concentration. A composite Heat Vulnerability Index (HVI) was developed using Principal Component Analysis (PCA) to integrate these environmental and demographic variables into a spatial heat-risk surface. Results indicate a substantial rise in LST (>5 °C), particularly across urban centers such as Kushtia Sadar and Khoksa, alongside a consistent decline in NDVI and NDWI, signifying degradation of green and blue spaces. Correlation analysis revealed strong negative relationships between NDVI–LST and NDWI–LST, underscoring the mitigating role of vegetation and surface moisture. PCA results confirmed that vegetation–moisture interactions dominate environmental variability, while demographic concentration exerts a secondary yet persistent influence. High and very high heat-risk zones expanded from 211.89 km² in 2010 to 424.42 km² in 2024, reflecting intensifying spatial thermal stress. The findings represent an environmentally weighted spatial screening of heat risk rather than a comprehensive socio-ecological vulnerability assessment. The study highlights priority areas for nature-based adaptation strategies, including urban greening, waterbody restoration, and reflective surface planning, to reduce localized heat exposure in rapidly urbanizing regions of Bangladesh.
This paper aims to examine the association of the specific governance indicators, which are the regulatory quality, the rule of law, and government effectiveness, on sustainable development in 60 countries around the world in 2024. This is explained by the key role that the state institutions and the institutional framework may play in enhancing economic, social, and environmental results and in achieving the Sustainable Development Goals (SDGs). The study employed the quantitative approach that is based on 2024 cross-sectional data. The data were obtained from the 2024 SDG Index and the World Bank's Worldwide Governance Indicators (WGI). The Eviews software was used to compute an Ordinary Least Squares (OLS) multiple linear regression model to examine the relationship between the independent variables (regulatory quality, the rule of law and government effectiveness) and the dependent variable (the SDG Index). The results reflected that the rule of law and the efficacy of the government have a positive and substantial effect on sustainable development, but the regulatory quality did not show a direct significant impact. This shows that sustainable development is based on the unity of the institutional framework which consolidates legal, regulatory, and administrative potential to achieve quantitative results.
To determine the suitability of the soils of the Fallujah and Karma regions for agricultural purposes, a field study was conducted. Soil samples were taken to a depth of 30 cm from a number of pedunclear soils in the study area. They were characterized morphologically, physically, and chemically, and were then classified accordingly. Based on the 2015 Food and Agriculture Organization (FAO) classification system, spatial distribution maps of selected soil characteristics were generated using ArcGIS 10. The analysis relied on the SYS 1980 coordinate system to determine and visualize the spatial extent of soil suitability across the study area. The results showed that the soils of the study area are distributed between the group of advanced desert soils and desert sedimentary soils. The soil textures of the study area are distributed between medium to coarse textures within the alluvial, sandy, and sandy loam types. The study also showed that the salinity in the area is distributed into four class, and is divided into three class in the distribution of gypsum and lime ratios. The suitability results for the soils of the study area showed the presence of four types: (N) and (N1), which are unsuitable, type (S4) which is slightly suitable, and (S3) which is moderately suitable. The main determinants in the study area are soil salinity and the proportions of gypsum and lime.
The transition from fossil fuels to renewable energy is vital for addressing climate change and ensuring energy security. Hybrid renewable energy systems (HRES), particularly those integrating solar photovoltaic (PV) and wind power, have emerged as a promising solution to overcome the intermittency and variability of individual sources. This study develops a comprehensive simulation and optimization framework for hybrid PV–wind systems, incorporating advanced energy storage options such as lithium-ion batteries and ultracapacitors. Using high-resolution meteorological and load data, both grid-connected and off-grid configurations are analyzed to evaluate system reliability, cost-effectiveness, and adaptability across different climates. A special focus is given to Kuwait, where high solar irradiance and moderate wind resources align with national energy diversification goals under Kuwait Vision 2035. The results highlight the technical and economic feasibility of hybrid systems, showing significant improvements in energy yield, load matching, and levelized cost of energy (LCOE) compared to standalone technologies. Furthermore, the study underscores the importance of intelligent control strategies, advanced component technologies, region-specific optimization, and explicit planning and performance evaluation insights in ensuring sustainable and resilient deployment of hybrid renewable systems.
Effective maintenance planning in high-performance mechanical systems requires a structured approach to identifying and prioritizing potential failure modes under multiple, often conflicting criteria. Conventional Failure Mode and Effects Analysis (FMEA) relies heavily on subjective judgment, which can limit consistency and transparency in decision-making. To address this limitation, this study develops a decision-oriented framework that integrates Shannon entropy-based weighting with three Multi-Criteria Decision-Making (MCDM) methods, namely SAW, TOPSIS, and VIKOR. The framework is applied to a representative high-performance mechanical system, in which maintenance-related factors, including failure probability, detection capability, economic impact, repair time, and resource availability, are evaluated in a unified structure. Entropy weighting is employed to derive criterion importance directly from data, reducing reliance on expert bias. The combined use of multiple MCDM techniques enables cross-validation of ranking outcomes and improves the robustness of the prioritization process. The results show a high degree of consistency among the three methods (Spearman’s $\rho>0.80$), indicating stable identification of critical failure modes. The proposed framework provides a transparent basis for risk-informed maintenance planning and supports more effective allocation of inspection and repair resources. From an engineering management perspective, the approach facilitates the transition from experience-driven decisions to data-supported strategies, contributing to improved system reliability and operational efficiency. Although demonstrated in a specific application context, the framework can be extended to other engineering systems where structured failure prioritization is required.
Optical Character Recognition (OCR) plays a crucial role in the digitization and preservation of textual information; however, for low-resource languages such as Kashmiri, reliable OCR solutions remain largely unavailable. Kashmiri, primarily written in the Perso-Arabic (Nastaliq) script, poses significant challenges due to its cursive structure, extensive use of ligatures, complex diacritical marks, and limited availability of annotated datasets. This research aims to address these challenges by developing a functional OCR system specifically tailored for Kashmiri text. The proposed system is built using the open-source Kraken OCR engine and leverages deep learning techniques with transfer learning from a pre-trained Arabic OCR model. A synthetic dataset was generated using Unicode Kashmiri text, enriched with Kashmiri-specific diacritics and exclusive characters, and rendered into images through automated text-to-image pipelines. Extensive preprocessing, augmentation, and iterative fine-tuning were performed to improve recognition accuracy. Model performance was evaluated using standard metrics such as Character Error Rate (CER) and Word Error Rate (WER) on both seen and unseen data. Experimental results demonstrate a substantial improvement over the initial model, with character accuracy increasing from 54.91% to 79.91% and word accuracy improving from 4.65% to 44.19%. The final model shows strong recognition capability for common and Arabic script characters, while Kashmiri-specific inherited diacritics remain a challenging area. In addition, a cross-platform user interface developed using Flutter enables users to upload or capture images and obtain digitized Kashmiri text through a simple and accessible workflow. Rather than proposing a new recognition architecture, this work contributes empirical insights, reproducible methodology, and error characterization for OCR in a previously unsupported low-resource Nastaliq language. This work is positioned as a baseline OCR system for printed Kashmiri Nastaliq text at the line level and does not claim state-of-the-art performance.
This study explores the fluctuations in temperature and precipitation in Chandrapur, Maharashtra, over the last 30 years from 1991 to 2024. The recorded data suggest an increase in temperature, particularly in the summer months from March to May. In addition, winter nights are gradually warmer. Furthermore, the quantity of rainfall is also changing; less rain is observed in June and August, yet an increase is seen in July and September. Not only are these fluctuations evident, but they also showcase the true and escalating impacts of climate change in the area. The Chandrapur district is an industrial and agrarian hub. Therefore, there is an urgent need to devise and prioritize climate adaptation policies.
For Authors
For Reviewers
For Editors
Open Access
