Search Results (118)

Plastic pollution is a growing environmental and social concern, particularly in Southeast Asia, where urban rivers serve as key pathways for transporting waste to marine environments. This scoping review examines 110 peer-reviewed studies to understand how plastic pollution in waterways is being researched, addressed, and reconceptualized. Drawing from the literature across environmental science, technology, and social studies, we identify four interconnected areas of focus: urban pollution pathways, innovations in monitoring and methods, community-based interventions, and interdisciplinary perspectives. Our analysis combines qualitative synthesis with visual mapping techniques, including keyword co-occurrence networks, to explore how real-time tools, such as IoT sensors, multi-sensor systems, and geospatial technologies, are transforming the ways plastic waste is tracked and analyzed. The review also considers the growing use of novel theoretical frameworks, such as post-phenomenology and ecological materialism, to better understand the role of plastics as both pollutants and ecological agents. Despite progress, the literature reveals persistent gaps in longitudinal studies, regional representation, and policy translation, particularly across the Global South. We emphasize the value of participatory models and community-led research in bridging these gaps and advancing more inclusive and responsive solutions. These insights inform the development of plastic tracker technologies currently being piloted in Vietnam and contribute to broader sustainability goals, including SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

This paper presents an Industrial–Ecological Symbiosis Framework that enables industrial operations to achieve quantifiable ecological gains without compromising operational efficiency. The model integrates Mixed-Integer Linear Programming (MILP) with AI-optimised forecasting to allow real-time adjustments to production and resource use. It was tested across the apparel manufacturing, metalworking, and mining sectors using publicly available benchmark datasets. The framework delivered consistent improvements: fabric waste was reduced by 10.8%, energy efficiency increased by 15%, and carbon emissions decreased by 14%. These gains were statistically validated and quantified using ecological equivalence metrics, including forest carbon sequestration rates and wetland restoration values. Outputs align with national carbon accounting systems, SDG reporting, and policy frameworks—specifically contributing to SDGs 6, 9, and 11–13. By linking industrial decisions directly to verified environmental outcomes, this study demonstrates how adaptive optimisation can support climate goals while maintaining productivity. The framework offers a reproducible, cross-sectoral solution for sustainable industrial development. Full article

The integration of artificial intelligence into sustainable agriculture holds significant potential to transform traditional agricultural practices. This transformation of agricultural practices through AI directly intersects with several critical sustainable development goals, such as Climate Action (SDG13), Life Below Water (SDG 14), and Life on Land (SDG 15). However, such implementations are fraught with multifaceted challenges. This study explores the technological, organizational, and environmental challenges confronting top management in the agricultural sector utilizing the technological–organizational–environmental framework. As interest in AI-enabled sustainable initiatives continues to rise globally, this exploration is timely and relevant. The study employs an interpretive case study approach, drawing insights from a carbon sequestration project within the agricultural sector where AI technologies have been integrated to support sustainability goals. The findings reveal six key challenges: sustainable policy inconsistency, AI experts lacking farming knowledge, farmers’ resistance to change, limited knowledge and expertise to deploy AI, missing links in the existing system, and transition costs, which often hinder the achievement of long-term sustainability outcomes. This study emphasizes the importance of field realities and cross-disciplinary collaboration to optimize the role of AI in sustainability efforts. Full article

Achieving Sustainable Development Goals (SDGs) remains a critical global challenge. This study analyses the environmental priorities related to SDGs 12, 14, and 15—interlinked and focused on responsible production and consumption, life below water, and life on land respectively—reflected in political party manifestos from the 2019, 2022, and 2024 Portuguese general elections, assessing their alignment with the SDGs and broader European political ideologies. A content analysis reveals significant disparities in attention across these goals, with SDG 15 receiving greater prominence than SDGs 12 and 14. Findings highlight the influence of political ideology, showing left-wing parties emphasize all three SDGs more consistently than their right-wing counterparts. These results underscore the need for a more balanced and comprehensive political commitment to sustainability. By exploring the interplay between national and European political agendas, this research provides valuable insights for aligning environmental policies with the UN 2030 Agenda and fostering transformative change in sustainability governance. Full article

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning. Full article

With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), “Life Below Water”. Full article

Nuclear accident-derived radionuclide dispersion poses critical challenges to marine ecological sustainability and human–ocean interdependence. While existing studies focus on hydrodynamic modeling of pollutant transport, the link between nuclear safety and sustainable ocean governance remains underexplored. This study investigates radionuclide diffusion patterns in semi-enclosed bays using a high-resolution coupled hydrodynamic particle-tracking model, explicitly addressing threats to marine ecosystem stability and coastal socioeconomic resilience. Simulations revealed that tidal oscillations and topographic constraints prolong pollutant retention by 40% compared to open seas, elevating local concentration peaks by 2–3× and intensifying bioaccumulation risks in benthic organisms. These findings directly inform sustainable marine resource management: the identified high-risk zones enable targeted monitoring of fishery resources, while diffusion pathways guide coastal zoning policies to decouple economic activities from contamination hotspots. Compared to Fukushima’s open-ocean dispersion models, our framework uniquely quantifies how semi-enclosed geomorphology exacerbates localized ecological degradation, providing actionable metrics for balancing nuclear energy development with UN Sustainable Development Goals (SDGs) 14 and 3. By integrating hydrodynamic specificity with ecosystem vulnerability thresholds, this work advances science-based protocols for sustainable nuclear facility siting and marine spatial planning. Full article

Extreme weather events from climate change challenge infrastructure stability. While water-related engineering enhances disaster resilience, it also impacts ecosystems. Taiwan has implemented Ecological Check and Identification (ECI) since 2003, yet challenges remain in standards, resource allocation, and effectiveness. This study analyzes 35 coastal engineering cases and participated in two engineering projects from five key perspectives. The results show that there are regional differences in the types of projects implemented for ECI. Landscape engineering was the main type in northern Taiwan (31%), water resource engineering was the main type in southern Taiwan (43%), and no cases were found in eastern Taiwan. Most inspections occur in the proposal (24%), planning (22%), and design (22%) stages, with limited post-construction monitoring (14%). Furthermore, ecological assessments were lacking in 49% of cases, and aquatic ecosystems were underrepresented. Inconsistent inspection formats and low species documentation (57% of cases) reduce data comparability and conservation effectiveness. To address these gaps, some recommendations were made, including standardizing inspections, integrating Sustainable Development Goals (SDGs), promoting low-carbon approaches, strengthening public participation, and establishing long-term monitoring. The findings provide policy insights to enhance ECI, supporting sustainable coastal engineering while balancing infrastructure benefits and environmental conservation. Full article

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges. Full article

Socioscientific issues are a key aspect of science education, enhancing citizens’ understanding of the intricate relationships among global concerns and fostering their engagement in informed decision making on these problems. To this end, teachers must be able to establish connections between scientific content, its application in everyday life, and its impact on social, economic, and environmental dimensions. This study analyzes the factors that influence teachers’ ability to address these topics in the classroom. It includes two studies. The first study (n = 213) examines prospective science teachers’ interest in and perceived knowledge of 14 issues related to the Sustainable Development Goals (SDGs). The second study (n = 135) analyzes the types of arguments that participants use to justify their interest. A mixed-method ex post facto design was employed, using ad hoc questionnaires. The results suggest significant differences between interest and perceived knowledge across certain specific topics. Additionally, the topic addressed tends to evoke specific dimensions within arguments, with cultural/social and ecological/environmental aspects being the most prevalent, influencing the connections teachers establish with everyday life contexts. These findings highlight how interest, perceived knowledge, and the topic itself influence the dimensions considered in argument construction when discussing socioscientific issues and may contribute to the development of teacher training programs that foster a deeper understanding of the complex nature of these sustainability-related issues. Full article

Polyethylene microplastics (PE MPs) pose a severe threat to aquatic ecosystems and human health, demanding urgent, sustainable remediation strategies. While the electro-Fenton process is widely used for treating refractory pollutants in wastewater, its standalone application remains inadequate for PE MPs due to their stable chemical structure and complex molecular chains. This study introduces a green and sustainable magnetite-activated persulfate electro-Fenton (Mt-PS-EF) system designed to address these limitations while aligning with circular-economy principles. By synergizing Fe₃O₄ catalysis, persulfate activation, and electrochemical processes, the Mt-PS-EF system achieves efficient PE MP degradation through hydroxyl (·OH) and sulfate (SO₄·⁻) radical-driven oxidation. Under optimized conditions (60 mg/L PE, 40 mM persulfate, 150 mg Fe₃O₄, 20 h treatment), a 90.6% degradation rate was attained, with PE MPs undergoing chain scission, surface erosion, and release of low-molecular-weight organics. Crucially, the magnetic property of magnetite facilitated the recovery and reuse of the catalyst, significantly reducing material costs and minimizing waste generation. By integrating catalytic efficiency with resource recovery, this work advances scalable, eco-friendly solutions for microplastic pollution mitigation, directly contributing to UN Sustainable Development Goals (SDGs) 6 (Clean Water) and 14 (Life Below Water). The findings highlight the potential of hybrid electro-Fenton technologies in achieving sustainable wastewater treatment and plastic waste management. Full article

Renewable energy has emerged as a transformative and essential alternative in the global energy sector. Many countries are striving to achieve the Sustainable Development Goals (SDGs) established by the United Nations for 2030, particularly the goal of ensuring that all individuals have access to clean and affordable energy. This paper re-examines the impact of economic growth (EG), trade openness (TO), exchange rates (ER), foreign direct investment (FDI), green finance (GF), and oil prices (OL) on renewable energy consumption (REC) across 14 Southern African countries: South Africa, Botswana, Lesotho, Namibia, Tanzania, Madagascar, Mauritius, Kenya, the Comoros, Zambia, Eswatini, Rwanda, Angola, and Mozambique, during the period of 2000 to 2022. This study employed cointegration and unit root tests, as well as the RALS-EG and MMQR models, to estimate the long-run relationships among the variables. The results reveal that renewable energy consumption is positively and directly related to economic growth, trade openness, exchange rates, green finance, and foreign direct investment across all quantiles (q05–q95), with no evidence of asymmetric effects. These findings suggest that economic growth, green finance, and foreign direct investment are crucial for fostering renewable energy innovation in Southern African countries. Policymakers are encouraged to prioritize strategies that enhance these factors as a foundation for achieving sustainable energy solutions. Full article

This study examines the impact of sustainable waste management on the quality of life of the inhabitants of Croatian island communities, focusing on how waste management practices contribute to sustainable tourism development. This study aims to provide policymakers and local stakeholders with insights into the implementation of effective waste management strategies that improve environmental protection and the well-being of residents. This research aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) by promoting sustainable waste management in island communities, SDG 12 (Responsible Consumption and Production) by encouraging waste reduction and recycling, and SDG 14 (Life Below Water). This study uses a survey-based quantitative research method, collecting data from 585 residents of the Kvarner islands using a structured questionnaire. The hypotheses are tested, and the relationships between waste management practices, quality of life, and sustainable tourism development are looked at using partial least squares structural equation modeling (PLS-SEM). This study concludes that sustainable waste management, driven by the active participation of residents in waste separation and recycling, significantly increases the quality of life of residents and supports the sustainable development of tourism on the Kvarner islands. This study concludes that effective waste management supported by community participation is crucial for improving the quality of life of residents and promoting sustainable tourism on the Croatian islands. It emphasizes that integrating sustainable waste management practices into tourism development policies can conserve environmental resources and ensure the long-term well-being of communities. Full article

The integration of sustainability in education has gained global attention as a critical component of achieving the United Nations Sustainable Development Goals (SDGs). Within the United Arab Emirates (UAE), significant efforts have been made to incorporate sustainability into national policies, reflecting the country’s vision for sustainable economic, social and environmental development. Within the context of Education for Sustainable Development (ESD), this research aims to investigate the alignment between national sustainability policies and their practical implementation in secondary schools, with a focus on identifying barriers and proposing actionable recommendations to enhance the integration of sustainability into education. This study employs a qualitative case study design with content analysis of data gathered from interviews and focus groups collected from a total of 21 teachers, school leaders, heads of departments and government officials, alongside the review of 14 relevant key policy documents. Key findings include a gap between policy and implementation, lack of a unified framework, resource disparities, and several barriers and strengths. This research concludes with recommendations to address these challenges, so that the UAE can strengthen its position as a leader in sustainability education, further aligning its national vision with global SDGs. Full article

The monitoring of regional development sustainability is closely linked to the development of an indicator system that best meets stakeholders’ requirements, providing a solid foundation for strategic decision-making. In pursuit of progress in achieving the Sustainable Development Goals (SDG), efforts are continuously being undertaken to refine and enhance the indicator framework. Implementing interdisciplinary approaches for a comprehensive assessment of sustainable development in regions allows for a swift expansion and augmentation of data on regional transformations. An important aspect of the study of sustainability at the regional level is the additional possibility of using unstructured news content through text mining methods. The issue of applying natural language processing techniques for Russian-language sources is significant, as a large number of relevant tools are developed for English. Additionally, the analysis of news content has several features that complicate the classification of sentiments of messages with mostly neutral wording. The proposed methodology for processing specific news content in assessing the sustainability of regional development was implemented. An application for data scraping was developed, data were collected taking into account the selected regions and periods, stop word dictionaries were configured, frequency analysis was implemented, and the sentiment analysis of the obtained slices was carried out. For the formed set of news documents related to sustainable development by keywords according to SDGs 1–17, for the regions of the Volga Federal District, a corpus of documents was obtained representing data for 2021, 2022, and 2023 for 14 regions. The analysis of key topics for different areas and periods was carried out using the cosine similarity measure. The developed approach to news analysis allows for increasing the efficiency of monitoring on various topics. This methodology has been tested for systemic and operational assessment in the dynamics of the sustainable development of regions. Text analysis methods within the framework of decision support at the regional level provide the opportunity to identify emerging trends. Full article