Artificial light at night (ALAN) has become a problem for fireflies because it disrupts their natural processes and threatens the conservation of their populations. In this regard, the aim of the study was to determine the effects of ALAN on firefly species through a systematic review. The PRISMA 2020 statement was fundamental for the review of the databases, and the inclusion and exclusion criteria for specifying the subject of study. On the other hand, the annual growth of scientific production was determined using the digital tool (Calcuvio). The year and country with the highest scientific production were 2021 and the United States, respectively, and the annual growth (2005−2025) was 16%. The most studied species was Lampyris noctiluca, and the effect of ALAN on the most common fireflies was a change in the intensity and frequency of their flashes in females. It is concluded that investment should be made in research in countries with abundant and diverse populations of fireflies. Furthermore, studies should be conducted on trophic interactions or sublethal physiological effects of fireflies, as well as on diversifying the species under study.

This study aimed to demonstrate the application of environmental activity-based costing (EABC) and its impact on supporting environmental sustainability, in accordance with ISO 14001 and 14051 standards for material flow cost accounting (MFCA) and GRI 300 standards for materials, energy, water, compliance, waste, and environmental performance improvement. EABC is an environmental accounting tool that identifies activities and allocates environmental costs to those activities, then to products, thereby assigning each product its actual costs and providing more accurate data. The research was conducted at the General Company for Fertilizer Industries in the Southern Region of Basra, Iraq. The researcher employed a practical approach by comparing the system implemented in the company under study with EABC. The main reason for using this technique is the inefficient use of resources and the resulting environmental pollution and fines imposed for exceeding permissible pollution limits. These costs have come to constitute a large percentage of the company’s total costs, thus impacting its profitability. The research contributed to identifying areas of waste resulting from the inefficient use of available resources and assisted management in making sound and accurate decisions related to environmental and economic aspects. It also helped improve environmental performance and enable the allocation of environmental costs to products based on their resource consumption. This, in turn, leads to the sustainability of resources through optimal use, thus achieving environmental sustainability. The study concluded that adopting cash flow statements helps improve various administrative decision-making processes, including pricing decisions, by allocating environmental costs to products and the activities that generate them. Furthermore, some reasons for waste in raw materials are attributed to the poor quality of those materials and the manual addition of materials. Therefore, the model directs management’s attention and efforts towards purchasing less environmentally damaging materials and using a pump for material application.

Lakes in mining areas face serious ecological degradation due to complex interactions between human activities, land use change, and industrial pressures. Globally, approximately 46.7% of lakes have lost their ecosystem resilience, with impacts such as declining water quality, sedimentation, heavy metal pollution, and biodiversity loss. While previous studies have mostly focused on post-mining pit lakes, limited attention has been given to conservation in active mining areas, leaving a critical research gap. This study aims to identify the factors influencing lake water resource conservation in mining regions, analyze the interrelationships among these factors, develop a conceptual model, and propose contextual strategies for sustainable conservation. A systematic literature review was conducted following the PRISMA 2020 protocol, using searches on Scopus and Web of Science for English-language publications from 2015 to 2025. Inclusion criteria emphasized empirical studies addressing lake conservation in mining areas. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT) version 2018, and data synthesis employed thematic analysis with NVivo 14 to identify key themes, factor relationships, and model design. From an initial 642 articles, 114 studies met the criteria. The analysis identified 13 key factors, with three dominant determinants: human–environment interaction, eco-friendly technology and innovation, and socio-economic pressures. Factor relationships included direct pathways such as institutional capacity and social capital, mediating roles such as environmental education and leadership, and negative moderation through economic pressures. The resulting conceptual model emphasizes integrating technological interventions, social capacity building, and environmental value internalization. Priority strategies include environmental education, institutional strengthening, community participation, and adoption of mitigation technologies. Overall, lake conservation in mining contexts requires an integrative social–ecological systems approach that balances technical innovation, social interventions, and mitigation of economic drivers.

Kerosene pollution, stemming from its widespread use as a fuel and solvent, poses significant health and environmental risks. This study aimed to isolate biosurfactant-producing Klebsiella pneumoniae from petroleum-contaminated soil and apply the biosurfactant to enhance kerosene biodegradation. Among twelve isolates screened, seven produced biosurfactants, with K. pneumoniae S9 exhibiting the highest emulsification index (E24 = 45%). The biosurfactant was extracted, purified, and characterized as a lipopeptide via Thin-Layer Chromatography (TLC) and Fourier Transform Infrared (FT-IR) spectroscopy. Supplementation with the biosurfactant significantly accelerated kerosene degradation, achieving 64% efficiency within an 11-day incubation period. These results demonstrate the potential of this biosurfactant as an effective agent for the bioremediation of kerosene-contaminated environments.

Padang City faces serious waste problems, including a 500-ton increase in daily waste generation to 500 tons and an annual accumulation of 236,296 tons (2023). Waste from the Final Processing Site is predicted to exceed its maximum limit by 2026; waste composition mainly comprises organic materials (62.53%) and plastics (13.6%), which have not been sufficiently managed through the Reduce, Reuse, and Recycle (3R) paradigm. This study analyzes the institutional, technical, regulatory, financial, and participatory barriers to waste management in Padang, as well as the policy implications from collaborative governance and circular economy perspectives. Using qualitative-descriptive methodology, with document analysis and policy evaluation, this study offers a unique contribution by combining polycentric governance defined as multi-level coordination and activity among government, private sector, and community actors with responsive regulation that situates punitive enforcement in the context of observed social behaviour and institutional capacity. The results indicate that institution fragmentation, under-enforcement of established laws, unsustainable funding mechanisms, and low community participation undermine the waste management practices in Padang. Integrated Waste Processing Place 3R and waste banks have, so far, not achieved optimal scale in terms of effectiveness. Contextualizing these outcomes through the lenses of polycentric governance, responsive regulation, circular economy, and community-based social marketing shows the role that cross-sectoral collaboration, participatory mechanisms, and adaptive regulatory tools played in building resilient urban waste systems. Theoretically, this study contributes to environmental governance scholarship by integrating governance design and regulatory innovation in the Global South context, while offering practical recommendations for performance contracts among stakeholders, as well as the adoption of Extended Producer Responsibility (EPR), decentralized technologies for organic waste, and digital-based incentives at the community level. Therefore, this study not only highlights the need for structural reforms but also contributes to establishing inclusive, adaptive, and sustainable waste management systems in Indonesia’s urban areas.

Landfill leachate poses a major challenge to urban waste management, particularly in tropical regions with high rainfall and heterogeneous waste composition. This study developed an artificial neural network (ANN) based on a multilayer perceptron (MLP) architecture to predict leachate volume at the Supit Urang landfill in Malang City, Indonesia. The dataset combined primary measurements of leachate discharge with secondary meteorological and environmental data, including rainfall, temperature, humidity, wind, and waste volume. Data preprocessing involved cleaning, imputation, transformation, and normalization to improve data quality and model readiness. The ANN model used two hidden layers with 64 neurons each and was optimized with the Adam algorithm, early stopping, and L2 regularization to balance predictive accuracy and generalization. The model achieved an R$^2$ of 0.61 and correlation coefficients above 0.82, indicating a good ability to capture nonlinear relationships and overall leachate trends. However, the relatively high root mean square error (RMSE) values showed that individual predictions still deviated substantially from observed values. Overall, the findings indicate that ANN models are promising decision-support tools for sustainable landfill management, although further improvements in data quality and model optimization are still required. The study also offers practical insight for estimating leachate generation and planning treatment strategies in urban landfills.

Natural radioactive nuclides $^{238}$U, $^{232}$Th, and $^{40}$K present in brick manufacturing facilities pose potential environmental, health, and economic concerns. This study employed gamma-ray spectroscopy using a NaI(Tl) detector to accurately determine radionuclide activity concentrations in ten samples collected from brick factories, Iraq. The investigation evaluated several critical health risk parameters, including radium equivalent activity, excess lifetime cancer risk, absorbed dose rate, and gamma representative index. The measured specific activities for $^{238}$U, $^{232}$Th, and $^{40}$K ranged from 32.67 ± 1.22 to 34.87 ± 1.26 Bq/kg, 24.65 ± 0.92 to 38.84 ± 1.16 Bq/kg, and 405.76 ± 4.91 to 419.92 ± 5.01 Bq/kg, respectively. All calculated radiation hazard indices were found to be within the permissible limits established by international regulatory organizations as recommended by Organisation for Economic Co-operation and Development (OECD), United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), and International Commission on Radiological Protection (ICRP). The findings indicate that natural radioactivity levels in these facilities pose no significant health risks. Specifically, both occupational workers and the surrounding population remain protected under current operational conditions. These results provide important baseline data for radiation safety assessment in the brick manufacturing industry and demonstrate compliance with international safety standards.

This study aimed to develop a predictive model of water availability using artificial neural networks (ANN) in the Chalcas River basin, located in the district of San Pedro de Palco, Ayacucho, Peru. A quantitative, predictive, and non-experimental longitudinal design was applied. Hydrological data were used, including monthly average precipitation (ranging from 3.44 mm in June to 123.49 mm in February), weighted crop coefficients (Kc), monthly evapotranspiration (ETo), and a drainage density of 5.81 km/km$^2$. A multilayer ANN was structured and trained over 2000 epochs, achieving an average accuracy of 90.62% and a normalized mean absolute error (MAE) of 0.0528. The model determined the flow rate for the period 2003–2030 period, identifying critical seasonal patterns: a peak of 317.45 l/s in January 2028 and a minimum of 28.55 l/s in July 2026. These findings highlight the need to implement water storage strategies during wet seasons and optimize water use during dry periods. Ultimately, the ANN-based model enhances water resource management, reduces scarcity-related risks, and promotes the sustainability of the irrigation system. This methodology demonstrates broad applicability and can be replicated in other basins facing similar hydrological challenges, using the ANN model.

Urban waste management requires a data-driven approach to understand community characteristics as a basis for designing public service information systems. This study aims to analyze the social dimensions of household waste management in Gorontalo City as a basis for the pre-design stage of the waste management information system. The research design used a cross-sectional survey of 400 households selected through stratified random sampling in nine subdistricts. Data were collected through a structured questionnaire covering sociodemographic characteristics, types of waste produced, and waste disposal behavior. The analysis was conducted descriptively and inferentially using the Chi-square and Cramer's V tests. The results show that household waste is dominated by organic waste (73%) and plastic (24%). The most common disposal behavior is through government transportation services (46.5%) and public trash bins (31.8%), while burning waste (15.0%) and disposal into rivers/open spaces (6.3%) are still found. Although there were minor variations in the contingency table between socio-demographic groups, the Chi-square test results showed that gender, age, and education were not significantly related to waste type and disposal behavior ($p$ $>$ 0.05). This indicates that waste management behavior is relatively homogeneous across all social groups. These findings reinforce the need for a universal service approach to waste management and provide an empirical basis for the development of the Gorontalo City SIMS, which focuses on improving service access, reporting disposal points, public education, and the integration of waste banks and city waste facilities.

The plastic waste is the promising environmental pitfall faced across the globe, no matter what India is not exempted. Today we are having digital nativity among the Gen z or iGeneration leads to diverse environmental behavioral pattern. The study was focused area of Bangalore the reason which it is filled with the multi-cultural and diverse community. The study collects the structured questionnaire considering 942 samples. The novelty of the article through a light on identification of major information sources influencing behavioral change, gender-based differences in environmental concern, and limited awareness of health impacts. The methodology incorporated Cronbach Alpha, factor analysis, correlation, and analysis of variance (ANOVA) to ensure empirical rigor and interpret complex relationships in behavior and awareness. The study also limelight to the policy makers to leveraging the educational institutions to mandate to conduct the sustainable drive practices among the growing iGeneration.

This research examines the preparedness of individuals in Indonesia’s green building sector to utilise digital construction technologies, including Internet of Things (IoT), building information modelling (BIM), and artificial intelligence (AI). The objective is to enhance energy conservation and efficiency. The research integrates Unified Theory of Acceptance and Use of Technology (UTAUT2) and Task-Technology Fit (TTF) to develop a model that assesses the readiness of green construction teams to implement digital tools to enhance energy performance. The Partial Least Squares Structural Equation Modelling (PLS-SEM) approach is employed to determine reliability, validity, and structural correlations. The final model accounts for 93.0% of the variance in behavioural intention (BI), 34.4% in use behaviour (UB), and 44.2% in performance expectancy (PE). BI is a robust predictor of actual usage ($\beta$ = 0.586, $p$ $<$ 0.001). Social influence (SI) ($\beta$ = 1.037, $p$ $<$ 0.001), perceived value (PV) ($\beta$ = 1.300, $p$ $<$ 0.001), PE ($\beta$ = 0.181, $p$ = 0.0049), and habit (HB) ($\beta$ = 0.283, $p$ = 0.047) all positively affect BI. Conversely, facilitating situations exert a significant negative impact ($\beta$ = -1.584, $p$ $<$ 0.001). When individuals excessively rely on organisational assistance, they diminish their intrinsic motivation. TTF is a significant predictor of PE ($\beta$ = 0.665, $p$ $<$ 0.001); however, it does not directly influence BI. The integration of technology into tasks is primarily driven by individuals’ perceptions of its performance advantages rather than by direct adoption. This study focuses on the unique requirements of green-construction processes, in which digital technologies contribute to reducing energy consumption, an approach notably different from prior UTAUT2 + TTF studies. The research presents a model illustrating how task alignment, performance perceptions, and the evaluation of costs against benefits influence individuals’ readiness to adopt digital technology in green building project initiatives.

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.

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.

This study aims to explore the utilization of waste glass (cullet) and bentonite clay as additives to improve the physical and mechanical properties of building bricks made from low-grade clay materials of West Kazakhstan. The research addresses both environmental challenges related to glass waste recycling and the efficient use of locally available raw materials in ceramic production. During the research, clay samples from the Rubezhinsk deposit and bentonite clay from the Pogodayev field were used. Two types of three-component mixtures were prepared: (1) loess clay-bentonite powder-waste glass, and (2) loess clay-bentonite suspension-waste glass. Additives were varied between 5%−25% for bentonite and 5%−15% for waste glass. Standard procedures, including semi-dry pressing, thermal analysis, and scanning electron microscopy, were employed to evaluate the chemical composition, microstructure, plasticity, drying sensitivity, compressive and flexural strength, frost resistance, and water absorption. The introduction of bentonite and cullet significantly enhanced the performance of the ceramic mass. Due to its colloidal properties, the bentonite suspension led to better compaction, lower porosity, and higher compressive strength (up to 13.2 MPa) compared to powder-based mixtures. Microstructural analysis revealed the formation of albite and anorthite crystalline phases that acted as reinforcing agents. The modified mixtures showed reduced drying sensitivity, improved frost resistance, and lower water absorption. The optimized composition of loess clay with bentonite suspension and cullet is a promising solution for producing high-quality, eco-friendly ceramic bricks. The use of waste glass enhances the bricks’ technical properties and contributes to sustainable waste management practices in Kazakhstan.