Search Results (13,796)

This study examines how digital transformation in higher education institutions (HEIs) contributes to public value creation, moving beyond efficiency-oriented narratives toward broader societal outcomes. Using a PRISMA-ScR approach, the study systematically reviews 47 peer-reviewed articles published between 2013 and 2025 across major academic databases. The review maps the evolution of scholarship and identifies the key mechanisms through which digital transformation influences public value. The findings reveal three interrelated dimensions shaping outcomes: digital competencies, institutional readiness, and governance alignment. Digital competencies enable the effective adoption and use of technologies, while institutional readiness—comprising digital infrastructure, leadership capacity, and organizational culture—acts as a mediating condition influencing implementation success. Governance alignment, including regulatory coherence, accountability mechanisms, and stakeholder engagement, plays a moderating role in determining whether digital transformation initiatives generate inclusive and socially beneficial outcomes. In addition to positive outcomes such as improved access, service quality, and transparency, the review identifies critical risks—including digital inequality, data governance challenges, and algorithmic bias—that may constrain public value creation, particularly in resource-constrained and Global South contexts. Building on these findings, the study develops the Global Digital Transformation—Public Value Creation (G-DTPVC) framework as an evidence-synthesized model derived from the reviewed literature. The framework specifies key constructs, causal relationships, and indicative measures to support future empirical research and policy application. By linking digital transformation processes in HEIs to broader public value outcomes and Sustainable Development Goals (SDGs 4, 9, and 16), this study advances theoretical understanding and provides actionable, context-sensitive guidance for policymakers and institutional leaders seeking to foster inclusive, accountable, and resilient higher education systems. Full article

Achieving sustainable development requires decoupling economic growth from environmental degradation. In this context, this study examines the effects of tourism arrivals on CO₂ intensity and energy intensity, two key indicators of environmental sustainability aligned with SDGs 7 and 13. Panel autoregressive distributed lag (ARDL) and nonlinear ARDL models are employed using a balanced panel of 54 countries over the period 1996–2023. In addition, Wald tests for long-run asymmetry, dynamic multiplier analysis, and Dumitrescu–Hurlin causality tests are applied. The results confirm the existence of stable long-run relationships between tourism arrivals and both CO₂ intensity and energy intensity. In the symmetric framework, tourism growth is associated with significant long-run reductions in CO₂ and energy intensity, while short-run effects are negative and significant only for CO₂ intensity. In the asymmetric framework, positive tourism shocks generate stronger and more persistent reductions in both intensity measures, whereas negative shocks lead to weaker environmental efficiency gains. Moreover, the Wald test shows the existence of long-run asymmetry between positive and negative tourism shocks. In addition, the dynamic multiplier analysis confirms that environmental intensity adjusts gradually over time following tourism shocks. Finally, Dumitrescu–Hurlin causality tests indicate bidirectional Granger causality relationships between tourism arrivals and environmental intensity indicators. The findings are robust to dynamic endogeneity, the COVID-19 shock, and country heterogeneity. Overall, the findings indicate that tourism arrivals contribute to lowering long-term environmental intensity, consistent with relative decoupling and the goals of sustainable tourism development. Full article

Bangladesh is an extremely climate-exposed country, with erosion, accretion, tidal surges, and cyclones continuously modifying coastal districts. Shoreline change in Bangladesh is crucial for sustainable coastal management and disaster resilience. Therefore, the objectives of this research are as follows: (i) to assess accretion- and erosion-based shoreline changes of the Bangladesh delta adjacent to the Bay of Bengal for 2021–2025 using a fixed 2021 reference shoreline and a 2025 shoreline proxy extracted from Landsat 8/9 imagery, and (ii) to explore onshore change dynamics from satellite-derived NDVI, NDBI, and NDWI for 2022–2025. The study covers 14 coastal districts and integrates the 2021 baseline shoreline, Survey of Bangladesh geospatial datasets, and 17,055 Ground Reference Points (GRPs) to support geometric consistency and spatially explicit reporting at the delta scale. Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to assess vegetation health, surface water distribution, and built-up/exposed land characteristics. Results indicate spatial variability in coastal change, with 383.49 km² of land gained through accretion and 124.12 km² lost to erosion, resulting in a neat accretion of 259.37 km² between 2021 and 2025; 8747.91 km² remained geomorphologically stable. Spectral index trends show minimal inter-annual NDVI and NDWI variability, suggesting stable vegetation cover and no long-term expansion of surface water. In contrast, a slight increase in NDBI indicates localized exposure of new sediments or small-scale land-use transitions along emerging coastal zones. Spearman correlation analysis highlights consistent negative relationships between NDVI and NDWI and moderate contrasts between NDVI and NDBI, reinforcing the coexistence of vegetation recovery, water withdrawal, and sediment-driven land emergence. The novelty of this study lies in the provision of consistent, near-real-time coastal change inventory for the full ~710 km Bangladesh delta coastline by combining a common 2021 baseline shoreline with harmonized Landsat 8/9 OLI surface reflectance (2022–2025) and linked onshore spectral-index dynamics over the same period. Overall, this short-term assessment reveals a sedimentary system that is active but balanced, with accretion surpassing erosion despite cyclone-affected disturbances, underscoring the value of operational satellite monitoring for coastal management, hazard preparedness, and climate-adaptive planning. Full article

Repurposing shipping containers to construct modular buildings is an emerging trend that contributes to a more sustainable building sector. In the tourism sector, they enable low-impact, relocatable accommodation adaptable to diverse environments, reducing their ecological footprint. The feasibility of using this kind of structure for self-sufficient tourist accommodation has not yet been thoroughly explored. This work focuses on the case study of the Versatile Cabin, a modular building made from end-of-life shipping containers. It provides a comprehensive analysis of its thermal performance and the capability of maintaining comfortable indoor conditions without relying on the electricity grid. Using TRNSYS, the thermal demands of the dwelling are evaluated across 45 different Spanish locations, taking into account the climatic diversity of the country. Additionally, the study explores the integration of a photovoltaic system to supply power for the HVAC equipment, revealing potential for self-sufficiency, particularly in southern locations with lower heating demand. The results indicate that the PV system can meet between 88.5% and 99.9% of the dwelling’s electricity needs, with an average of 96.1%. Overall, the findings offer valuable insights into the thermal performance and self-sufficiency of modular buildings within the tourism sector, aligning with sustainable building practices and sustainable development goals. Full article

This manuscript creates a framework for decision support based on green urbanism to direct the sustainable development of Gamasa, an Egyptian seaside city. The paper aims to convert the concepts of green urbanism into a multi-criteria evaluation that can support strategic urban development and prioritize spatial interventions. Sustainable mobility, green and blue infrastructure, energy and resource efficiency, urban form and density, social livability and public space quality, and governance and implementation feasibility are the six dimensions that are defined. These dimensions are derived from international sustainability literature and tailored to Gamasa’s particular challenges. The study’s methodology combines a multi-criteria decision-making approach based on the AHP with spatial analysis of land use, street hierarchy, building shape, and green space distribution. Weights for these dimensions are determined by expert-based pairwise comparisons, which are backed by a SWOT analysis. To prioritize priority zones for green transformation, the weighted framework is applied to four important urban areas: residential districts, a large urban park, the waterfront, and the main urban corridor. The top priorities, according to the results, are climate-responsive coastal design, increased green and blue infrastructure, and sustainable transportation. For quickly urbanizing coastal cities, the method demonstrates how the AHP operationalizes green urbanism into quantifiable, context-sensitive goals. Full article

Heritage-led urban redevelopment is increasingly adopted to advance cultural continuity and social vitality; however, its long-term sustainability is often compromised due to the absence of user-oriented assessment methods. Conventional Post-Occupancy Evaluation (POE) approaches are limited in their ability to capture experiential, social, and participatory dimensions of architectural and urban spaces. This study examines the potential of Virtual Reality (VR) as a participatory POE tool for sustainable heritage redevelopment through the case study of Souq Al Muharraq in Bahrain. A convergent mixed-method approach is employed, integrating immersive VR 360-degree walkthroughs, structured questionnaires, qualitative semi-structured interviews, and expert evaluation. The findings reveal significant discrepancies between design intentions and lived experience, specifically in thermal comfort, circulation, social usability, and informal spatial practices. The study demonstrates that VR supports a user-centered and experiential approach aligned with Sustainable Development Goals (SDGs) 9, 11, and 16. It further proposes a sustainable and cost-efficient framework for architecture and urban projects’ evaluation by enabling early and post-user-centered evaluation of projects to reduce costly revisions and the creation of inclusive, adaptive, and resilient architecture and urban spaces. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. Full article

This study investigated the attitudes and intentions towards the use of inclusive practices among teachers in Saudi schools, with a particular focus on the role of professional training in shaping these factors. Teachers are key to the implementation of inclusive education, and their training and attitudes are essential to effective teaching. Data were collected from 175 teachers, with participants completing the Attitudes to Inclusion Scale, Intention to Teach in Inclusive Classroom Scale, and Inclusive Practice Scale. The results indicate that teachers with more positive attitudes towards inclusion were more likely to express stronger intentions to teach in inclusive classrooms. The findings also highlighted the importance of professional development, emphasising the need for comprehensive training that addresses both theoretical and practical aspects of inclusion. There is a need for teacher training programmes that integrate both special education and inclusive teaching strategies to support teachers’ readiness to implement inclusive education in their classrooms. Full article

This study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps and technological readiness levels. Results indicate frequently detected emerging contaminants including pesticides, antibiotics and antidepressants in surface water, groundwater and wastewater systems. Advanced analytical methods, particularly liquid chromatography coupled with high-resolution mass spectrometry, stands out as the main detection technique, allowing the identification of trace levels of contaminants. These techniques also support the identification of pollution patterns associated with agriculture, urban and industrial effluents. However, significant asymmetries persist between international and Portuguese research. Particularly evident in systematic monitoring networks and integrated risk assessment approaches. Conventional water/wastewater treatment plants show limited removal efficiency, while advanced oxidation processes, adsorption technologies and microalgae-based systems demonstrate promising but variable performance depending on scale and operational maturity. The findings highlight gaps between scientific advances and regulatory implementation, emphasizing the need for strengthened monitoring frameworks and technology scale-up strategies. They also call for improved integration between science, governance, and sustainability policies to ensure resilient water resource management in line with the Sustainable Development Goals. Full article

Program KBLBB was implemented to reduce carbon emissions and mitigate climate change by 2030. Total sales of Battery Electric Vehicles (BEVs) in Indonesia until June 2025 are 107,428, with the increase in sales resulting in a proportional rise in EV battery waste. EV battery waste requires comprehensive policy recommendations for its management, as in Indonesia. The goal of this research is to develop a sustainable assessment for an EV battery waste management model that addresses environmental, economic, and social perspectives. The assessment is carried out using the End-of-Waste framework model, Reuse, with recycling technology hydrometallurgy for Nickel Manganese Cobalt (NMC) and Lithium Ferro Phosphate (LFP) batteries. The results show that the environmental impacts of waste from NMC batteries are 20% smaller than those of LFP batteries, with 80% of the impacts. The total cost of waste from LFP batteries is lower than that of NMC batteries. The S-LCA risk score shows the same results for waste from NMC and LPF batteries: a very high risk for actual female employment, unequal remuneration, no collective bargaining indicators, and no right to organize. Sensitivity analysis results for EV battery waste management model for NMC batteries with hydrometallurgy, collection level of 30%, and recovery rate of 85%. Full article

Improving the climate adaptability of farmers is crucial to ensuring agricultural production and achieving the goal of sustainable development in agriculture. Against the background of climate change aggravating agricultural risks, how do farmers’ own risk attitudes affect their adaptive behavior? Based on the micro-survey data of 480 grape growers in the Turpan-Hami Basin in 2025, we used the least squares method (OLS) to explore the impact of risk appetite on the climate adaptation behavior of farmers and its mechanism. The study found that risk appetite significantly promoted the adoption of adaptive behaviors by farmers. For every 1 unit increase in the risk preference score, the number of climate-adaptive behaviors adopted by farmers increased by an average of 0.322. Mechanism testing shows that both formal credit and informal credit play a partial intermediary role. The intermediary effect accounts for 18.3% and 36.3% respectively, and the transmission effect of informal credit is stronger; Institutional trust and interpersonal trust both positively regulate the relationship between risk preference and adaptive behavior at the level of 1%. Research shows that we should take into account risk education and production environment optimization, pay attention to the supplementary role of private lending, and build a multi-level trust promotion system to jointly improve the climate adaptability of farmers. Full article

Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article

The rapid expansion of subway systems has led to significant carbon emissions during station construction, yet a systematic and interpretable framework for evaluating low-carbon performance across different construction methods remains underdeveloped. To address this gap, this study proposes a comprehensive evaluation model that integrates a pressure–state–response (PSR) framework with an entropy-weighted TOPSIS method. A multi-dimensional indicator system comprising 17 indicators was established, covering material and energy consumption (pressure), environmental carbon states (state), and management responses (response). The entropy weight method was employed to determine objective indicator weights, and the TOPSIS method was used to rank the overall low-carbon performance of different construction schemes. An empirical study of a subway station in Guangzhou, China, was conducted to compare three construction methods: open-cut, top-down cover excavation, and reverse cover excavation. The results demonstrate that the reverse cover excavation method achieves the highest low-carbon performance. Electricity consumption and concrete-related emissions were identified as the most influential factors, while obstacle analysis revealed key constraints for carbon reduction. The proposed PSR–entropy–TOPSIS framework offers a transparent, data-driven decision-support tool for optimizing construction schemes, contributing to the sustainable development goals of urban rail transit projects. Full article

In the context of the dual-carbon goals and the broader United Nations 2030 Agenda for Sustainable Development, stimulating innovation motivation within environmental protection enterprises holds significant strategic importance for achieving long-term sustainability. Drawing on institutional theory and signaling theory, this study examines how government subsidies influence the sustainable innovation performance in China’s environmental protection industry and investigates the boundary conditions and mechanisms of this relationship from a socio-economic and integrated policy perspective. Using a sample of 121 listed environmental protection enterprises in China from 2016 to 2025, this paper empirically analyzes the impact of government subsidies on both the quantity and quality of innovation output. It innovatively incorporates the market-driven factor of public environmental attention into the analytical framework to test its moderating effect and examines the mediating role of corporate social responsibility. The findings indicate that government subsidies significantly enhance both the quantity and quality of innovation output from environmental protection enterprises, thereby contributing to their sustainability transition. Public environmental attention positively moderates the innovation-incentivizing effect of government subsidies, with a stronger moderating effect on innovation quality than on quantity. Heterogeneity analysis reveals that the incentive effect of government subsidies on innovation quantity is significant only in the eastern and western regions of China, while the effect on innovation quality is more pronounced in state-owned enterprises and the western region, offering insights for region-specific and ownership-specific sustainable policy designs. Mechanism analysis indicates that government subsidies promote innovation performance by encouraging firms to fulfill corporate social responsibilities, with CSR serving as a partial mediator. These findings extend institutional and signaling theories to the context of environmental protection enterprises and provide a framework for quantifying and monitoring the effectiveness of sustainability policies. Based on the conclusions, relevant policy optimization suggestions are proposed to align industrial innovation with the principles of sustainable development. Full article

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications. Full article