Search Results (1,866)

This study explores the potential of Digital Earth Africa (DE Africa) coastlines products for assessing the Coastal Vulnerability Index (CVI) along the Casablanca coastline, Morocco. The analysis integrates remotely sensed shoreline data with elevation, slope, and geomorphological information from ASTER GDEM and geological maps within a GIS environment. Shoreline change metrics, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), Linear Regression Rate (LRR), and End Point Rate (EPR), were used to evaluate erosion trends from 2000 to 2023. Results show that sandy beach areas, particularly those below 12 m elevation, are highly exposed to erosion (up to 1.5 m/yr) and vulnerable to coastal hazards. Approximately 44% and 23% of the study area were classified as having very high and high vulnerability, respectively. The results indicate that remotely sensed data and GIS techniques are valuable and cost-effective tools for multi-scale geo-hazard coastal assessment studies. The study demonstrates that DE Africa products, combined with local landscape data, provide a valuable tool for coastal vulnerability assessment and monitoring in Africa. Full article

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

Harmonic drive is susceptible to strength failure and performance degradation failure during operation. Given the long life test cycles, limited sample size, and incomplete understanding of degradation laws, this study conducted comprehensive life test and performance degradation test research to enable future failure prediction and reliability assessment for harmonic drive. Building upon established test rigs for a HD life test and performance degradation test, a step-down accelerated life test methodology was developed. Life tests under step-down accelerated conditions were executed, with a concurrent performance degradation test throughout the life test. Key datasets acquired include vibration signal histories, degradation data for critical performance indicators such as stiffness, precision, transmission efficiency, and backlash. Test results show that the predominant strength failure in the atmospheric environment is flexspline fatigue fracture, while significant degradation occurred in stiffness, precision, and backlash across all test conditions; transmission efficiency showed gradual degradation before strength failure followed by a marked decline post-failure. Finally, the failure mechanism of strength and performance degradation is analyzed, and the mechanism consistency of the two failures is verified by Hilbert envelope spectrum analysis and degradation trajectory shape consistency, respectively. The results from this paper provide critical data support and a reference foundation for the proactive maintenance of the harmonic drive. Full article

The North China Plain faces severe water scarcity, and the efficient use of brackish water has become a crucial pathway for sustaining agricultural development. In this study, we combine scenario analysis with Data Envelopment Analysis to establish a multi-scenario efficiency evaluation framework. Focusing on six counties in Handan, Hebei Province, we employ an input-oriented Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model to systematically evaluate brackish water irrigation efficiency (BWIE) across a baseline year (2020) and eight projected scenarios for 2030. The results show that the mean efficiency values across scenarios range from 0.646 to 0.909. Scenarios combining universal adoption of water-saving irrigation with normal hydrological conditions achieve the highest mean efficiency (>0.9), with minimal regional disparities and optimal system stability. The promotion of water-saving irrigation technologies is the primary driver of improved BWIE, whereas simply increasing brackish water application yields only limited marginal benefits. Redundancy analysis further indicates that water resource inputs are the main source of efficiency loss, with brackish water redundancy (42.3%) far exceeding that of land inputs (10.5%). These findings provide quantitative evidence and methodological support for optimizing regional water allocation and advancing sustainable agricultural development. Full article

The sustainable development of society requires the incorporation of environmental, social, and governance (ESG) principles. While ESG assessments are widely used in corporate settings, their application in healthcare settings, such as hospitals, remains underexplored. This study aimed to develop a comprehensive evaluation framework integrating ESG and digital transformation (DX) with traditional hospital efficiency and effectiveness assessments. Using open data, financial reports, and hospital website scraping, we applied a slack-based model (SBM) of data envelopment analysis (DEA) and super-efficiency SBM-DEA to calculate sustainability scores across four dimensions: overall sustainability, efficiency, effectiveness, and ESG/DX performance. Results showed that all three components—efficiency, effectiveness, and ESG/DX—were positively associated with overall sustainability. However, ESG/DX performance negatively impacted profitability in smaller hospitals, and improved effectiveness in rehabilitation hospitals was linked to higher operational costs. These findings suggest that while ESG and DX contribute to long-term sustainability, their short-term financial burden may challenge certain hospital types. The proposed index provides valuable insights for hospital management and policy development, aiming to advance ESG and DX initiatives in healthcare. Full article

The management of municipal solid waste (MSW) plays a crucial role in advancing sustainable development and circular economy goals across the European Union. In Italy, despite improvements in separate collection, significant regional disparities in MSW performance and costs persist. This study assesses the eco-efficiency of MSW services in 5516 Italian municipalities to uncover performance gaps and their underlying drivers. Eco-efficiency is measured using a Data Envelopment Analysis (DEA) model based on the Generalized Directional Distance Function (GDDF). This model incorporates per capita cost as an input, sorted waste as a desirable output, and residual waste as an undesirable output. A second-stage quantile regression is then utilized to explore how contextual factors influence eco-efficiency across various performance levels. The results reveal significant territorial disparities, with only 0.13% of municipalities achieving full eco-efficiency. Paradoxically, higher levels of separate waste collection—typically a policy goal—are associated with increased costs, especially in more efficient municipalities, suggesting a trade-off between environmental performance and economic sustainability. Similarly, population density negatively affects eco-efficiency but may facilitate economies of scale in collection systems. These findings highlight a tension between achieving optimal sorting rates and maintaining cost-effectiveness. Policy interventions should consider these trade-offs, prioritizing basic performance in lagging areas while promoting cost-control strategies in high-performing municipalities. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

Background: Orthodontically driven osteogenesis (ODO) is a surgical tunnel modification of periodontally accelerated osteogenic orthodontics (PAOO), combining selective corticotomy with bone grafting in sequential and/or segmental fashion. This is a minimally invasive approach that enhances periodontal health and allows orthodontic tooth movement beyond the original alveolar envelope. Considering the lack of long-term three-dimensional data on orthodontically driven osteogenesis (ODO), this study aims to quantitatively assess the long-term stability of alveolar bone and buccal cortical thickness following ODO, using CBCT imaging. The null hypothesis is that ODO does not result in significant changes in alveolar bone volume or cortical thickness over a seven-year follow-up period. Methods: Twenty patients (13 females, 7 males; mean age 27.4 ± 5.3 years) who had undergone orthodontically driven osteogenesis (ODO) using a minimally invasive tunnel approach and segmental corticotomy protocol followed by clear aligner therapy were retrospectively evaluated. The mean follow-up period after treatment was 7 years (range: 5–15 years). Cone beam computed tomography (CBCT) scans were obtained at one year postoperatively (T1) and again at the long-term follow-up visit (T2). Buccal bone thickness measurements were taken at standardized levels (3 mm, 5 mm, and 7 mm apical to the cementoenamel junction) and compared between T1 and T2 to evaluate bone stability over time. In addition, histologic evaluation of the previously grafted area was performed in two patients: one sample was collected during an alveolar ridge augmentation procedure six months after ODO, and the other during orthognathic surgery eight months after ODO. The samples were analyzed to assess new bone formation and integration of graft material. Results: Radiographic analysis showed long term stability of the new bone support. Histologic examination showed newly formed lamellar and reticular bone. Bone marrow showed no inflammatory infiltration, and bone particles were still detectable but incorporated in the newly created bone. Conclusions: Based on these findings, ODO appears to be a promising technique that could induce stable bone osteogenesis. A larger cohort study can enhance the evidence of these promising results to popularize this technique. Full article

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making. Full article

This case study evaluates the efficiency of STEM-based sustainability initiatives at the University of Brasilia (UnB) using a Bootstrap Data Envelopment Analysis (DEA) approach. Twenty projects were analyzed based on input variables—team size, budget, and workload—and output variables—number of beneficiaries and published papers. The results indicate higher efficiency in the Mathematics and Civil Engineering departments, while Energy Engineering showed the lowest performance. A strong correlation (r = 0.78) was observed between budget and publication volume, but no significant relationship was found between the inputs and number of beneficiaries. SDG 4 (Quality Education) was the most frequently addressed, whereas SDG 16 (Peace, Justice, and Strong Institutions) and SDG 14 (Life Below Water) received less attention. The study identifies key areas for improvement, emphasizing the need for more balanced resource allocation and contextual awareness over sustainability priorities. It also offers an adaptive and replicable framework to other faculties or institutions seeking to optimize sustainability efforts through the lens of resource allocation optimization. Full article

This study evaluates the economic dimension of sustainable supply chain efficiency among Korean automotive suppliers using an SCOR-aligned two-stage Network Range-Adjusted Measure (NRAM) Data Envelopment Analysis (DEA) model. The framework separates performance into Stage 1 (internal operations: Plan/Source/Make/Deliver) and Stage 2 (external outcomes: sales and profitability), enabling stage-specific assessment of operational versus market-facing efficiency. Firm-level financial data for about 1200 suppliers annually from 2021 to 2024, spanning five sectors, were analyzed with descriptive statistics, visualizations, and non-parametric tests. Results show that Stage 1 efficiency was consistently high and stable, while Stage 2 efficiency was lower, more variable, and declined in 2022 and 2024, revealing vulnerability to systemic market disruptions. Overall efficiency mirrored Stage 2, underscoring the fact that downstream financial outcomes drive total performance. Rather than introducing a new methodology, the contribution of this study lies in applying an established two-stage NRAM DEA within an SCOR-aligned framework to a large-scale longitudinal dataset. This application provides sectoral and temporal benchmarks on a national scale, offering evidence-based insights into how structural interdependence and systemic shocks influence supply chain efficiency. While the scope is limited to the economic pillar of sustainability, the findings contribute contextualized benchmarks that can inform managerial practice and future research integrating environmental and social performance dimensions. Full article

Quality assurance (QA) in higher education has become increasingly vital in response to global competition, digital transformation, and evolving sustainability demands. This study examines the leading QA frameworks—namely the European Standards and Guidelines (ESG), the EFQM Excellence Model, and ISO 9001—while integrating advanced analytical methodologies, including Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), and Bayesian modeling, to propose a comprehensive framework for assessing university performance. Through empirical analysis and comparative case studies of internationally ranked universities, this study demonstrates that combining objective indicators with quantitative methods significantly improves institutional efficiency, transparency, and competitiveness. Additionally, the role of digital education, ESG-driven sustainability strategies, and AI-based student feedback systems emerge as being crucial to the effectiveness of QA practices. The results suggest that hybrid evaluation models—blending traditional QA principles with data-driven analytics—promote continuous improvement, optimize resource management, and enhance educational outcomes. This research ultimately highlights the growing relevance of advanced quantitative frameworks in modernizing QA systems and supporting universities in addressing dynamic global challenges. Full article

Carbon fees are scheduled to be levied in Taiwan, posing unprecedented challenges for the steel industry, given its high emissions and risk of carbon leakage. This study explores the potential impact of this policy on steel industry performance by incorporating projected carbon fees into the efficiency assessment. The Slacks-Based Measure (SBM) and Super SBM models in Data Envelopment Analysis (DEA), which account for negative data, are used to evaluate the operational efficiencies of 30 listed steel firms across supply chain segments in 2024 under baseline and carbon fee scenarios. Results reveal that incorporating the carbon fees mitigates the upward bias that overestimates inefficient firms’ SBM scores, triggers broad efficiency declines and ranking reshuffling (most severe upstream, moderate midstream, and least downstream), and widens cross-firm efficiency dispersion. Moreover, the study finds that excessive carbon fees and operating profit deficiencies are the main input- and output-side drivers of inefficiency, highlighting improvement potential in carbon cost management and profitability gains. To date, the efficiency implications of carbon fees for Taiwan’s steel industry have remained underexplored. Our findings offer empirical insights and a timely reference for steel firms to refine sustainability strategies ahead of forthcoming carbon fees. Full article

Effective assessment of power grid investment efficiency is crucial for optimizing resource allocation and improving operational performance. However, existing evaluation methods typically fail to account for two critical factors: inherent uncertainties in input–output data and temporal delays in investment returns. To address these limitations, this study introduces an integrated evaluation framework combining robust optimization techniques for uncertain variables with a time-lag Data Envelopment Analysis (DEA) approach to capture the multi-period dynamics and ensure resilience against external shocks and data perturbations. An empirical analysis conducted on panel data from 31 provincial power grid enterprises in China (2015–2023) reveals significant regional disparities in efficiency, particularly between coastal and resource-rich provinces. The findings highlight that excluding time-lag effects leads to systematic underestimation of efficiency and employing robust optimization yields more resilient efficiency scores amidst data uncertainties. The study contributes methodologically by advancing DEA frameworks to better reflect the complexities of power grid investments and empirically provides valuable insights for policymakers seeking to enhance investment strategies and achieve sustainable development goals. Full article

This article presents a systematic literature review of data envelopment analysis (DEA) models used to evaluate circular economy (CE) practices. The review is based on 151 peer-reviewed articles published between 2015 and 2024. By analyzing this collection, this review categorizes different DEA models and their levels of application, discusses the data sources utilized, and identifies the prevailing methodologies and evaluation criteria used to measure the CE performance. Despite the extensive literature on measuring the circular economy using DEA, a critical evaluation of existing DEA approaches that highlights their strengths and weaknesses is still missing. Our analysis shows that DEA models provide valuable insights when assessing circular strategies, namely, R2—Reduce, R8—Recycling, and R9—Recovering. Over 40% of the surveyed literature focuses on China, with nearly 20% on the European Union. Other regions are sparsely represented within our sample, highlighting a potential gap in the current research landscape. Full article