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Latest articles

Original scientific paper
Coupling Optimization of the Distillation and Absorption Columns and Its Application in Methanol-to-Olefins Process

Journal of Sustainable Development of Natural Resources Management, Volume 1, Issue 4, 12, 2025
Xiaohong Han, Ning Li, Guilian Liu

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Distillation and absorption are techniques used to separate mixtures, utilizing energy and absorbent materials as separation agents. These two processes are often combined for gas mixtures to enhance the recovery of key components while also reducing energy consumption. The operation of the distillation column influences not only its own performance but also that of the absorption column, and vice versa. A simplified model of combined absorption and distillation columns has been developed to analyse their performance concurrently. This model examines the relationship between the intermediate stream and the operating parameters of both units, yielding qualitative rules that can help minimize energy consumption and guide the optimization of the combined process. In the case of the methanol-to-olefins (MTO) process, the columns for demethanation and ethylene recovery have been qualitatively analysed using the proposed method, thus enabling efficient optimization of both columns. The optimal ethylene molar concentration at the top of the distillation column is found to be 0.5. Additionally, the corresponding reflux ratio is 1.6029, while the flow rates for the distillate and absorbent are 902.3 kmol/h and 900 kmol/h, respectively.
Original scientific paper
Analyzing the Impact of Food Access and Social Institutions on Farmers’ Food Sufficiency through Information Technology and Environmental Moderation in West Sumatra, Indonesia

Journal of Sustainable Development of Natural Resources Management, Volume 1, Issue 4, 12, 2025
Retwando Retwando, Bustari Muhctar, Yulhendri Yulhendri

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Farmers in West Sumatra face declining household food sufficiency, creating an urgent need for an integrated examination of the factors shaping their ability to meet dietary needs. This study is motivated by the limited evidence on how food access, institutional support, technology use, and environmental conditions jointly influence household food outcomes. It is hypothesized that food access has a direct effect on food sufficiency, while institutional roles influence food sufficiency indirectly through technological mediation, and that environmental conditions strengthen these relationships. The study uses a structured survey of 400 farming households analyzed with a structural equation modeling approach to assess direct, indirect, and moderating effects. The findings show that food access is the strongest determinant of food sufficiency, social institutions operate mainly through technology, and environmental quality enhances institutional effectiveness. The study concludes that food sufficiency depends on the combined strength of economic access, institutional capacity, technological use, and environmental resilience.
Original scientific paper
Influence of Atmospheric Moisture on Renewable Energy Generation in the Ecological Zones of Ghana

Journal of Sustainable Development of Natural Resources Management, Volume 1, Issue 4, 12, 2025
Rachel Olawoyin, George Owusu Amoah, Julia Quaicoe

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Climate change has intensified global interest in how atmospheric dynamics influence renewable energy systems. Ghana, aiming to expand renewable energy and achieve Sustainable Development Goal 7, faces rising atmospheric moisture across its ecological zones. This study examines how long-term humidity trends affect solar and wind power energy generation using meteorological and satellite data combined with correlation, regression, and spatial analysis to quantify the effects of relative humidity, cloud cover, dew point, temperature, wind speed, and precipitation from 1995 to 2024. Findings show that solar irradiance and photovoltaic performance are reduced by cloudiness and humidity in the Coastal Savannah, while the drier Guinea Savannah records higher irradiance and solar potential. The results confirm an inverse relationship between atmospheric moisture and solar power generation. The study underscores the importance of incorporating climatic factors into national energy planning and technological adaptation to optimise renewable energy systems across Ghana.
Editorial
Journal of Sustainable Development of Energy, Water and Environment Systems - Volume XIII

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 13, Issue 4, 12, 2025
Doris Beljan, Neven Duic

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The Journal of Sustainable Development of Energy, Water and Environment Systems (JSDEWES) is an international journal dedicated to the improvement and dissemination of knowledge on methods, policies and technologies for increasing the sustainability of development by de-coupling growth from natural resources and replacing them with knowledge based economy, taking into account its economic, environmental and social pillars, as well as methods for assessing and measuring sustainability of development, regarding energy, transport, water, environment and food production systems and their many combinations. In total 67 manuscripts were published in Volume XIII, all of them reviewed by at least two reviewers. The Journal of Sustainable Development of Energy, Water and Environment Systems would like to thank reviewers for their contribution to the quality of the published manuscripts.
Original scientific paper
Performance Assessment of You Only Look Once Models in Drone-Based Plastic Litter Quantification on Coastal Beaches

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 2, 06, 2026
Mounir Grari, Mimoun Yandouzi, Abdelaadim Khriss, Aissa Kerkour Elmiad, Mohammed Badaoui, Alae-Eddine Barkaoui, Yassine Zarhloule

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Plastic pollution represents a growing threat to global ecosystems and human well-being, creating an urgent need for advanced methods that support accurate monitoring and mitigation efforts. Recent technological developments have transformed environmental assessment practices, especially through the use of aerial systems combined with artificial intelligence. The integration of unmanned aerial vehicles with deep learning techniques offers considerable advantages by enabling the coverage of large areas while providing rapid and precise analysis of collected data. The You Only Look Once family of deep learning models has gained prominence for real-time object detection, tracking, and counting, making it suitable for large-scale environmental applications. This study evaluates multiple versions of the You Only Look Once model to determine their effectiveness in quantifying plastic litter on coastal beaches using aerial imagery. The performance of versions 8, 9, 10, and 11 is assessed in terms of detection, tracking, and counting accuracy using a custom dataset consisting of short aerial video sequences. All evaluated versions demonstrated high counting performance, with accuracy values exceeding 98%. Version 11 achieved the strongest results overall, including the shortest inference times. These outcomes highlight the potential of advanced deep learning–based detection systems to enhance automated environmental monitoring and support more efficient management of plastic pollution on coastal environments.
Original scientific paper
Predicting ESG Scores Using Firms' Financial Indicators: A Machine Learning Regression Approach

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 2, 06, 2026
Mariam Alsayyad, Sayed Mohamed Fadel

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The oil and gas industry plays a pivotal role in the global energy and financial markets. With increasing concerns surrounding Environmental, Social, and Governance (ESG) scores, their impact on this sector has become a growing area of focus. This study aims to forecast ESG scores in the oil and gas sector using extensive datasets comprising publicly available financial and ESG indicators of firms. The research analyzed data from 497 companies within the industry over a 12-year period. A total of 10 machine learning algorithms were utilized to predict ESG scores, including decision trees, ensemble methods (boosting, bagging, and voting), XGBoost, LightGBM, random forest, extreme random trees, linear regression, robust linear regression, and elastic net. The analysis incorporated a one-year lag in ESG scores and employed panel data regression techniques in machine learning. The findings demonstrated a high predictive performance, with the best R² value reaching 0.922. These results provide a practical framework for investors and decision-makers to evaluate a firm's ESG performance, facilitating more informed and sustainable investment decisions within the oil and gas sector.
Original scientific paper
Plastic Waste to Green Economy through Green Products and Green Packaging on Consumer Purchase Decisions of Micro Small Medium Enterprises in East Java Tourism

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Sumarmi Sumarmi, Yosini Deliana, Andi Dirpan, Eli Hendrik Sanjaya, Umar Haiyat Abdul Kohar

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Green packaging has emerged as a viable strategy to promote environmental responsibility and improve quality of life. This study investigates the effects of Green Products and Green Packaging on purchase decisions and their direct and mediated impacts on the Green Economy. Data were collected through questionnaires, observations, and interviews involving micro small medium enterprises in East Java’s tourism sector and local consumers. Structural Equation Modeling was employed for analysis. The results show that environmentally friendly products and environmentally friendly packaging significantly increase consumer purchase decisions, which subsequently strengthen green economic outcomes. Both variables also demonstrate positive direct effects on green economic development, while purchase decisions function as a partial mediator. The model indicates strong explanatory power and supports the view that sustainable product attributes shape consumer behavior. These findings highlight the importance of sustainable consumer behavior and offer strategic insights for micro small medium enterprises to strengthen eco-branding and accelerate green economic transitions.
Original scientific paper
Enhancing Urban Sustainability: Water-Saving Bio-Solar Green Roofs in Mediterranean Climates

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Behrouz Pirouz, Seyed Navid Naghib, Hana Javadi Nejad, Karolos Kontoleon, Patrizia Piro

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Green roofs are expanding due to their advantages but may face water deficits in the summer. However, bio-solar green roofs integrating renewable energy and green infrastructure could positively contribute to sustainable urban development and enhance environmental management. Therefore, the current study aims to experimentally investigate the benefits of bio-solar green roofs over conventional green roofs. The experiments were conducted in Rende, in southern Italy, which has a Mediterranean climate. The comparison of soil temperature and relative humidity aids in understanding temperature, water reduction, and the irrigation cycle in conventional green roofs and bio-solar green roofs. The analysis revealed that the soil surface temperature was higher in conventional green roofs than in bio-solar green roofs, with a difference of 4.8 °C to 9.8 °C, and exhibited the same trend in the case of bare soil. However, the bare soil humidity declined less than that of green roofs, possibly due to plant evapotranspiration. The analysis of the irrigation cycle in conventional green roofs and bio-solar green roofs shows a difference of 41.2%. While the relative humidity of conventional green roofs dropped from 97.7% to 32.5% at noon on the third day and fell below the 40% threshold of irrigation requirement for succulents, the relative humidity in bio-solar green roofs was higher than 73.7%. According to the outcomes, the bio-solar green roofs could have lower temperatures and less water consumption, enhancing the cooling impact of green roofs. The results will improve knowledge on the subject and be useful for policymakers in sustainable urban development by presenting a solution to real-world problems through practical and innovative building technologies.
Review paper
Dimensions and Determinants of Sustainable Public School Catering: A Systematic Literature Review

Journal of Sustainable Development of Sustainability Indicators, Volume 1, Issue 4, 12, 2025
Abderrahim Mounssif, Abdemajid Saidi

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Public school catering is a key instrument for promoting the sustainability of food systems, yet its analysis remains fragmented. This research proposes an integrated approach to identify the main determinants and dimensions of its sustainability. It highlights the central role of governance, regarded as the keystone of the system, operating through territorial anchoring. A systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method on the basis of 66 studies drawn from Scopus and Web of Science, made it possible to identify four interdependent dimensions: socio-cultural, economic, environmental and governance. The findings indicate that the overall performance of the system depends on the quality of governance, which strengthens territorial anchoring as a lever for sustainable improvement. The study thus proposes an innovative conceptual model and operational guidelines to support public decision-makers in fostering a more resilient and responsible school catering system.
Original scientific paper
Enhancing Reliability of Off-Grid Energy Systems through Combined Edge-Based Analytics and Predictive Maintenance Models

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
John Kimotho Nyambura, Joseph Kamau, Denis Mugambi Kaburu, Stephen Kinyua Gitahi

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Conventional energy generating strategies, such as reactive and scheduled maintenance, often lead to increased downtime, energy waste, and inefficiencies. This study integrates edge analytics with machine learning-based predictive maintenance to boost the reliability and sustainability of off-grid energy generating systems. Using Long Short-Term Memory and regression models, the approach enables early anomaly detection and fault prediction, reducing unplanned outages and maintenance costs. A comparative analysis between standard edge analysis and integrated edge-predictive methods shows that the integrated system achieves an accuracy of 91.6%, compared to the edge analytics model with an accuracy of 86.2% effectively stabilizing short-term fluctuations, generating fewer and more stable alerts, with a coefficient of determination R² of 0.98. Results highlight that combining predictive models with edge analytics enhances reliability, supports timely interventions, and strengthens system robustness in off-grid energy generating applications.
Review paper
Solar Technology Penetration in Sub-Saharan Africa: Status, Drivers, Barriers, and Future Prospects

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Christopher Warburg, Thomas Kivevele, Tatiana Pogrebnaya

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Sub-Saharan Africa's severe energy access deficit hinders its socio-economic development, affecting millions. This review is motivated by the need to understand the factors influencing the adoption of solar technology as a potential solution. It is hypothesized that while solar technology offers a viable path to electrification, its penetration is significantly influenced by a complex interplay of drivers and barriers. A systematic review of academic and grey literature was conducted to assess the current status, drivers, and barriers of solar technology penetration. Beyond synthesizing academic and grey literature, a structured Strength, Weaknesses, Opportunities and Threats analysis was conducted, followed by a pairwise comparison matrix and Analytic Hierarchy Process. The analysis reveals that financing barriers, weak grid infrastructure, and policy instability are the most important obstacles, while abundant solar resources, declining technology costs, and innovative business models act as major drivers. The Strength, Weaknesses, Opportunities, Threats–Analytic Hierarchy Process framework identifies and ranks strategic factors, generating actionable solution strategies such as blended finance, regulatory stability, and regional quality assurance mechanisms.
Original scientific paper
Analysis and design of the trajectory of water droplets from sprinklers using optical observation and mathematical modeling

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Aybek Arifjanov, Lukmon Samiev, Khumora Jalilova, Sirojiddin Jalilov, Umida Vokhidova, Elza Tursunova

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In this study, the trajectory of water droplets in a sprinkler irrigation system was studied using the optical tracking method. The analysis was carried out on the basis of 1920×1080 pixel, 60 frame/s video recordings taken in real field conditions. Image segmentation and trajectory detection algorithms were developed on the OpenCV and Python platforms to determine the trajectory of water droplets. The main focus of the study was to determine the dynamics of the movement of water droplets with a diameter of 0.002 meters after exiting a sprinkler head rotating at an angle of 360°. The following parameters were taken into account in the mathematical modeling process: exit velocity - 11 m/s, exit angle - 30°. Based on these data, the trajectory of the droplet was calculated using the equations of ballistic motion and air resistance (drag). The Random Forest model was used to assess the influence of factors. The results showed that the factors that have the greatest impact on the water spray trajectory are wind (31.2%) and terrain slope (25.6%). This means that small changes in wind speed and slope significantly reduce the water spray radius and cause uneven water distribution. The Convolutional Neural Network (CNN) model was used to spatially analyze and classify areas, achieving 93% accuracy in flat terrain and 74–79% accuracy in windy and uneven areas. This result indicates that the modeled system works with high reliability even in real field conditions. At the end of the study, the sprinkler exit angle and installation spacing were optimized, and the drift zones of water due to wind were reduced from 21.8% to 7.1%. This change has increased the stability of water distribution and allowed for a significant reduction in water consumption in crop production.
Original scientific paper
Experimental Validation of Zero Crossing Detection Sinusoidal Pulse Width Modulation for Grid Synchronised Inverters in Renewable Energy Systems

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Saloua Yahyaoui, Jalal Blaacha, Sanae Dahbi, Abdelhak Aziz, Abdelhafid Messaoudi

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This article presents a synchronization control of a sinusoidal voltage from a single-phase inverter powered by a photovoltaic chain, with the sinusoidal voltage of the electrical network. The control is based on the principle of a phase-locked loop. This elaborate control does not necessarily use the blocks of a phase-locked loop, but it exploits its fundamental condition of constant phase difference and also the only resulting consequence that manifests itself in the frequency equality between the inverter voltage and the electrical network voltage. The voltage at the output of the inverter is synthesized from the sinusoidal voltage of the electrical network according to a succession of simultaneous operations performed on this sine wave. Indeed, the sine wave of the network is made accessible for measurement by control electronics. Then, a shaping (square signal) of the sine wave is carried out by zero comparison; the comparator changes state at each zero-crossing detection of the sine wave. On each rising edge of the square signal, a train of width-modulated pulses is started which controls the single-phase inverter by the sinusoidal pulse width modulation control which synthesizes the signal with the same frequency as the network. The algorithm of this command is implemented in an 8-bit microcontroller. An inverter from the photovoltaic chain developed in the laboratory was tested at the standard grid frequency (50 Hz) and at other frequencies (50 Hz to 80 Hz) to evaluate the system’s ability to track frequency variations that may occur in practice due to grid disturbances. The manufactured inverter, equipped with the proposed phase-locked loop, successfully follows the electrical network. The steady-state frequency and phase errors remain below 1%, and the synchronization loop is established within 25 ms. The experimental results confirm the synchronization of the inverter to the power grid over a wide frequency range. Compared to classical synchronization techniques, often more complex to develop, the proposed phase-locked loop is a simple approach and suitable for implementation on standard microcontrollers controlling photovoltaic inverters under rapidly changing operating conditions.
Original scientific paper
Investigating Chemical Pollution from Agricultural Drainage in the Djoudj National Bird Park (Senegal)

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Ousmane Diankha, Mallé Gueye, Absa Ndiaye, Dado Ly, Samba Guissé, Ibrahima Gueye

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This study examines the extent and nature of chemical contamination in the Djoudj National Bird Park, a Ramsar-listed wetland situated in northern Senegal, with a focus on agrochemical residues originating from surrounding rice cultivation systems. Sediment samples collected during three seasonal campaigns were analysed for trace metals and a wide range of pesticides. Results revealed significant spatiotemporal variability, with peak concentrations of lead exceeding 7,000 µg/kg and alarming levels of banned pesticides, such as lindane and dicofol, reaching up to 200,000 µg/kg, detected at multiple sites. The data suggest strong links between agricultural drainage, irrigation cycles, and contamination pulses, particularly during the dry season. Correlations between salinity and metal concentrations suggest that hydrological conditions play a crucial role in determining pollutant mobility and bioavailability. The co-occurrence of persistent organic pollutants and modern pesticides underscores both historical and ongoing agrochemical inputs. These findings raise serious concerns for the ecological integrity of the park, particularly given its role as a major habitat for migratory birds and aquatic biodiversity. This study emphasises the need for the urgent implementation of mitigation strategies, including buffer zones, integrated pest management, and stricter regulatory enforcement, to ensure the long-term sustainability of this protected wetland.
Original scientific paper
Lessons from China’s Three-Decade Distributed Photovoltaic Development for Yemen

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Karamy Saeed

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Translating global distributed photovoltaic successes to Yemen’s fragile and conflict-affected context remains a critical challenge. This study develops a strategic roadmap by adapting lessons from China's three-decade market evolution, informed by comparative experiences from Germany, Japan, and the United States within a conflict-sensitive analytical framework. This research hypothesises that phased policy measures, community-based models, and international partnerships can assist in overcome systemic constraints in fragile environments. Through policy analysis, historical case studies, and barrier mapping, results demonstrate that phased pilot initiatives reduce investment risks, while stakeholder involvement enhances project sustainability. Adaptive tariff structures, scalable deployment models, and local involvement emerge as prerequisites for resilience in unstable settings. The study concludes with a conflict-sensitive, adaptive framework for the phased deployment of distributed photovoltaics in Yemen. This framework prioritizes approaches that synergistically advance reliable energy access, community resilience, and sustainable photovoltaics adoption. By integrating energy planning with local development imperatives, the proposed strategy offers a transferable model for fostering robust and sustainable energy systems in similarly crisis-affected regions.
Original scientific paper
Assessment of the Hosting Capacity of the Moroccan Electricity Grid for the Integration of Renewable Energies

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Wafae Arfaoui, Mouad Karmoun, Khaoula Azzakhnini, Mohamed Laarbi Elhafyani, Smail Zouggar, Hassan Zahboune, Taoufik Ouchbel, Adrian Alarcon Becerra, Nikola Matak

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This study examines how renewable energy integration influences power flow in Morocco’s national transmission grid. A detailed high-voltage network model was developed in GridCal using open-source data, and hourly simulations were carried out for four generation scenarios: a fully conventional system, an 8 percent solar case, a 12 percent wind case, and a 20 percent mixed renewable configuration representative of 2022. Results indicate that renewable generation alters both the magnitude and variability of transmission flows. Wind integration increased southern-corridor transfers from about 55 to 83 megawatts, while solar effects remained localized near production zones. These findings show that electrical connectivity and dispatch interactions, rather than geographic proximity, determine which corridors are most affected. The study provides a high-resolution framework for identifying transmission bottlenecks and supports Morocco’s energy-transition planning by linking renewable scenarios with grid-reinforcement priorities.
Original scientific paper
Production and Physicochemical Characterization of Biodiesel from Croton and Waste Cooking Oil Blends with Carbon-Based Nanoparticles

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Abdullahi Aden Hirabe, Meshack Hawi, Mariam Kassim Ali, Francis Njoka

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The search for sustainable alternatives to petroleum diesel has sparked interest in biodiesel production from nonedible feedstocks. This study investigated biodiesel derived from Croton oil and waste cooking oil under optimized transesterification conditions (oil-to-methanol ratios 1:1 and 2:1, 60 °C, 400 rpm, 60 min). To further improve the fuel quality, biodiesel was blended with diesel and doped with carbon-based nanoparticles (50–100 ppm). Physicochemical characterization of the fuels and nanoparticles was conducted using Fourier Transform Infrared Spectroscopy, Gas Chromatography–Mass Spectrometry, Scanning Electron Microscopy, Transmission Electron Microscopy, and X-ray Diffraction. Conversion efficiencies were reported as 88.13% for Croton oil and 90.96% for waste cooking oil. Viscosity decreased from ~5 mm²/s to ~3.62 mm²/s while the calorific value improved from ~35 MJ/kg to ~40 MJ/kg in pure biodiesel and nanoparticle-doped blends respectively. These improvements demonstrate that nanoparticle-doped biodiesel diesel blends can offer sustainable alternatives to petroleum diesel.
Review paper
Current State, Challenges, Recommendations and Prospects of Machine Learning Application in Fuel Cell Based Hybrid Electric Cars: A Comprehensive Overview

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Safa Slouma, Marwa Ben-Said Romdhane, Sondes Skander-Mustapha

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This paper reviews the current progress and outlook of various applications of machine learning techniques in the context of hydrogen fuel cell electric vehicles. Emphasizing the importance of efficient energy management systems, it presents a detailed comparative analysis of recent developments. Key research works on machine learning-based energy management systems are discussed, including the use of the Twin Delayed Deep Deterministic Policy Gradient algorithm, which has demonstrated up to 28% reduction in overall operational costs. Moreover, the review includes machine learning -based energy management systems approaches that account for fuel cell degradation, an area receiving growing attention due to its impact on performance and longevity. Notably, reinforcement learning strategies have achieved improvements in fuel economy of 5.7% and reductions in fuel cell degradation rates by 4.5%. In addition, robust machine learning -based prediction models are highlighted for their effectiveness in data-driven fault diagnosis, contributing to a 13.9% reduction in fuel usage during the Federal Test Procedure 75 Driving Cycle and a 14.32% reduction in the New European Driving Cycle, along with carbon dioxide emissions cut to less than 26.4%. Despite this progress, existing review papers seldom address the use of machine learning for predictive maintenance in the automotive sector, nor do they adequately consider functional safety aspects. This dual gap underscores the novelty and relevance of the present study. Furthermore, this work uniquely explores the integration of machine learning with smart traffic management systems to optimize hydrogen fuel cell electric vehicle operations and critically examines the defies and potential solutions for advancing hydrogen refueling infrastructure. The paper concludes by discussing major ongoing challenges and offering perspectives for future research in this transformative field.
Original scientific paper
Simultaneous Recovery of Ammonium and Phosphate from Wastewater Using Magnesium-Activated Temple Waste Biochar

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
I Made Wahyu Wijaya, I Ketut Sumantra, Ni Made Dharma Shantini Suena, Kailas Deoram Ahire, Pravin Mukund Nalawade

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Nutrient pollution from ammonium and phosphate in wastewater presents serious environmental challenges, including water eutrophication and resource inefficiency. The study aimed to analyse how magnesium-activated biochar made from temple waste could be used as a sustainable material for adsorping nutrients at the same time. The biochar was synthesised through pyrolysis and magnesium chloride activation, then applied to real wastewater from stabilisation ponds in Bali. The adsorption dynamics for both ammonium and phosphate were consistent with pseudo-second-order models, suggesting that chemical absorption was the prevailing mechanism. The Freundlich isotherm model provided the best fit, suggesting heterogeneous multilayer adsorption. Results demonstrated that adsorption capacity and rate varied across different wastewater sources, with the highest adsorption rates corresponding to ponds exhibiting more favourable surface interactions. Importantly, the adsorption rate constant for phosphate surpassed that of ammonium in some cases, emphasising the influence of ionic properties and biochar surface heterogeneity. The results confirm that biochar made from waste can effectively recover nutrients and should be included in circular approaches for managing wastewater.
Original scientific paper
Sustainable Digital Transformation in Public Administration: A Framework for University Enrollment

Journal of Sustainable Development of Energy, Water and Environment Systems, Volume 14, Issue 1, 03, 2026
Boglárka Eisinger Balassa, László Buics

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The increasing pressure on public institutions to adopt digital solutions has raised new questions about the actual sustainability of such transformations, particularly in administrative systems that remain resource-intensive despite partial digitalisation. While digitalisation is widely promoted as a path to greater efficiency and environmental responsibility, its sustainability outcomes are far from guaranteed. This study critically examines these assumptions through the case of a university enrolment system, identifying inefficiencies and exploring how digitalisation can optimise resource use and improve performance. The widely assumed link between digitalisation and sustainability is not universally valid, as outcomes depend on context, implementation, and infrastructure. The economic, environmental, and social impacts of digital transformation are assessed using process mapping, a conceptual sustainability framework, and estimates of paper and processing efficiency. Digital workflows are shown to reduce administrative workload, lower paper waste, and enhance accessibility, while underscoring the need to avoid unnecessary information technology practices, redundant communication flows, and poorly structured information management that may offset sustainability gains. For example, annual carbon dioxide emissions were reduced from 85,926 kg to 85,006 kg when transitioning from a hybrid to a fully digital enrolment process, primarily by eliminating paper-related emissions and reducing electricity use. The study provides a policy-oriented sustainability indicator framework to guide decision-makers in evaluating public sector digitalisation efforts, while also offering a structured approach to assess trade-offs and implementation conditions. The research contributes methodologically by combining process modelling with sustainability indicators – a rarely integrated approach in public administration – and lays a foundation for future computational optimisation models focused on institutional governance and sustainability-oriented digital transformation.

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