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.