Search Results (1,457)

In order to evaluate regional sustainability, a comprehensive framework is needed that can integrate a number of economic and environmental variables into a transparent and policy-relevant evaluation approach. The present study presents a data-driven informatics framework for the evaluation of Thai provinces that utilizes the additive weighting-based variant assessment algorithm (AWVAA) with Charnes–Cooper–Rhode (CCR)-based two-stage data envelopment analysis (DEA). The system allows three interrelated activities: provincial screening, representative decision-making unit selection, and comparative efficiency benchmarking of economic and environmental performance. AWVAA employs global and local simple additive weighting algorithms in screening 77 provinces to find representative units while keeping regional balance and data completeness. In the second phase, the selected provinces are evaluated by a two-stage DEA structure based on CCR to measure their relative efficiency for transforming development-related inputs into intermediate operational factors and ultimate economic and environmental outputs. The analysis starts with investment, tourist arrivals, and newborns as initial inputs, moves through energy use, electricity consumption, number of factories, and number of vehicles as intermediate variables, and ends with gross provincial product and air quality indicators, including ozone, PM10, and PM2.5 as final outputs. The proposed framework selects 16 typical provinces and shows significant variations in overall CCR efficiency and super-efficiency performance over the selected set. The results suggest that provinces with high screening-stage prominence may not necessarily become the strongest DEA-based standards and emphasize the complimentary roles of representative unit selection and formal efficiency assessment. The study combines multi-criteria screening with benchmarking based on DEA to give a transparent and replicable method for regional sustainability monitoring, comparative assessment, and evidence-based policy planning. The results provide an informatics-oriented paradigm for complicated regional evaluation and practical insights for enhancing sustainable provincial development in Thailand. Full article

Against China’s tightening cultivated land ecological constraints, clarifying how the New Rural Collective Economy (NRCE) affects Cultivated Land Use Ecological Efficiency (CLUEE) is critical for advancing agricultural sustainability in the Yangtze River Economic Belt (YREB). Using 714 panel data observations from 42 prefecture-level cities in the YREB from 2009 to 2025, this study measures CLUEE efficiency via SBM-DEA and quantifies the NRCE via the entropy weight method. Two-way fixed effects models estimate the impact, while two-step mediation analysis explores underlying mechanisms. Results show NRCE significantly boosts CLUEE, with each 1-unit increase in NRCE level driving a 0.1633-unit improvement in CLUEE, and findings remain robust after endogeneity correction. Mediation analysis reveals indirect effects via land-scale management, agricultural socialized services, and green technology innovation. This study innovatively clarifies the endogenous institutional driving effect of rural collective economic reform on CLUEE, filling the research gap of ignoring rural organizational institutional factors in existing CLUEE research. The findings provide empirical evidence and theoretical support for optimizing the construction of rural collective economic organizations and formulating differentiated cultivated land ecological governance policies in the YREB, so as to effectively unlock the ecological dividend of rural institutional innovation and facilitate coordinated ecological and economic development of cultivated land. Full article

As an information-guided environmental regulation method that can effectively improve farming practices, environmental training is widely used in the agricultural field. However, evidence on whether and how such training improves the green production efficiency of livestock farmers remains limited. This study investigates the effect of environmental training on the green production efficiency of hog farmers by explicitly accounting for spatial spillovers and exploring technology adoption as a mechanism pathway. Specifically, green production efficiency is first measured using the Super-SBM DEA model, and the spatial Durbin model is then employed to estimate both the direct effect and spatial spillover effect of training. The results of survey data from 371 hog farmers in China show that participation in training significantly enhances the green production efficiency of farmers, with positive spillover effects from neighboring farmers’ participation in training. Further mechanism analysis indicates that training promotes the adoption of clean production technologies, which in turn enhances green production efficiency. The positive effect of training is more pronounced among large-scale and better-educated farmers. Therefore, these findings suggest that policies should strengthen environmental protection training, promote the diffusion of clean production technologies, make better use of the demonstration households mechanism, and customize strategies to support the green transformation of hog farming. Full article

This study critically examines hospitality performance evaluation practices by proposing a narrative-based quantitative framework grounded in online reviews. While tourism experiences, particularly in transformative contexts, are understood as subjective and meaning-oriented, empirical evaluation remains reliant on standardized, growth-oriented indicators such as star ratings and satisfaction scores. To address this disconnect, online reviews are conceptualized not as post hoc satisfaction measures but as narrative expressions of evaluation. Drawing on sentiment analysis of narratives from eleven hotels, evaluations are operationalized through indicators and incorporated into a DEA framework. Efficiency is reframed as experiential conversion capacity—the ability of hospitality providers to transform material and organizational conditions into experiences perceived as meaningful by guests. Two aggregation configurations and a super-efficiency extension are applied to examine robustness and differentiation. The findings reveal divergence between narrative-based evaluations and star classifications, suggesting that rating systems fail to capture dimensions of meaning and affective resonance. Notably, a lower-category hotel emerges as the strongest in experiential positioning, challenging assumptions linking quality, classification, and value. From a sustainability perspective, the study contributes to critiques of tourism evaluation and supports post-growth, sufficiency-oriented approaches. Methodologically, it demonstrates how techniques can be repurposed as interpretive tools when grounded in narrative data. Full article

Wind power development is accelerating globally, leading to an imminent large-scale retirement of wind turbines and increasing the need for recycling infrastructure. This study proposes an integrated framework for recycling center site selection by combining Geographic Information System (GIS), AHP–CRITIC weighting, and Super-Efficiency Data Envelopment Analysis (DEA). A hierarchical GIS indicator system is constructed by incorporating environmental, locational, and social compatibility factors, including elevation, slope, land use, transportation accessibility, proximity to wind farms, and population-related constraints. GIS performs Euclidean distance, kernel density, and weighted overlay analyses to identify suitable areas, while indicator weights are determined through a hybrid subjective–objective approach. A Super-Efficiency DEA model is then applied, using labor and land costs as inputs and annual decommissioning quantities as output, to evaluate and rank candidate sites, with higher-ranked sites regarded as reliable locations. A case study in Xilingol, Inner Mongolia, verifies the method’s effectiveness. The proposed framework supports scientific planning for wind turbine recycling and promotes sustainable wind energy development. Full article

Background: European health systems face growing pressure from population ageing, post-pandemic service backlogs, and fiscal constraints. Yet substantial cross-country differences in health outcomes persist despite comparable levels of healthcare expenditure. This study evaluated the relative efficiency of European health systems using a multidimensional framework that integrates expenditure, prevention, and population health outcomes. Methods: A cross-sectional analysis was conducted on 25 European countries using 2022 data or the nearest available year. An output-oriented constant returns to scale Data Envelopment Analysis (DEA) model was estimated with two inputs, public and private healthcare expenditure per capita, and five outputs, life expectancy at birth, inverse infant mortality, healthy life years at birth, breast cancer screening coverage, and poliomyelitis vaccination coverage. A robustness specification added physician density as an additional input. Bootstrap bias correction with 1000 replications was applied to the baseline model. A second-stage Simar–Wilson truncated regression with 2000 bootstrap replications examined the association between inefficiency and selected contextual variables, including GDP per capita, population ageing, obesity prevalence, and tobacco use prevalence. Results: In the baseline DEA model, 8 of 25 countries were located on the technical efficiency frontier (Croatia, Czechia, Estonia, Greece, Hungary, Latvia, Lithuania, and Poland; output-oriented DEA inefficiency score = 1.000 for each country), while inefficiency scores among the remaining countries ranged from 1.042 to 2.617. The highest inefficiency scores were observed for Germany (2.617), Austria (2.283), Belgium (2.230), Ireland (2.219), and France (2.167). When physician density was added as an additional input, 12 countries were located on the estimated frontier. Bootstrap correction of the baseline model increased the estimated output-oriented inefficiency scores, with bias-corrected values ranging from 1.100 to 2.941. In the second-stage analysis, higher log GDP per capita was positively associated with bias-corrected inefficiency (coefficient 1.993; 95% bootstrap CI 0.219 to 4.197), whereas population ageing, adult obesity prevalence, and tobacco use prevalence were not statistically associated with bias-corrected inefficiency. Conclusions: In this cross-sectional sample of 25 European countries, higher healthcare expenditure was not consistently associated with frontier performance when health outcomes and preventive coverage were considered jointly. The results were sensitive to the inclusion of physician density and to bootstrap correction, supporting the interpretation of Data Envelopment Analysis as an exploratory benchmarking tool rather than a definitive ranking of health systems. These findings highlight the importance of assessing how financial and workforce resources are converted into measurable health and prevention-related outputs. Full article

Floodplain wetlands are globally important ecosystems, yet altered hydrological regimes increasingly disrupt the balance between woody and non-woody vegetation. In Australia’s regulated Murray–Darling Basin, it remains unclear whether woody plant encroachment represents a persistent shift toward terrestrialisation or a dynamic process that can be periodically reversed by flooding. This study quantified long-term patterns of woody-vegetation encroachment and retreat across 32,000 ha of mapped wetlands in the mid-Murrumbidgee River floodplain from 1988 to 2023, and assessed how hydrological variability and floodplain connectivity mediate these dynamics. Using open, analysis-ready Earth observation data from Digital Earth Australia (DEA) within the Open Data Cube (ODC) framework, we combined DEA Land Cover for transition mapping, Water Observations for hydrological masking, Landsat surface reflectance for Enhanced Vegetation Index (EVI)-based spectral plausibility testing, and the Wetlands Insight Tool for qualitative temporal context. Woody-vegetation dynamics were strongly non-linear and closely linked to alternating drought and flood phases. During the Millennium Drought (2001–2009), mapped woody-cover decline exceeded 50% of wetland area in some sub-regions, whereas the post-drought recovery interval (2008–2013) produced encroachment exceeding 40% in the most affected areas. Across the full 35-year record, mean encroachment rates ranged from 85 to 250 ha yr⁻¹ among sub-regions, summing to approximately 865 ha yr⁻¹ of woody expansion across the floodplain, while retreat rates were lower overall (approximately 634 ha yr⁻¹), resulting in a net expansion of woody cover. Local hydrological connectivity strongly mediated these responses: infrequently inundated wetlands showed persistent terrestrialisation, whereas more frequently inundated, better-connected wetlands experienced periodic flood-driven retreat. Landsat-derived EVI broadly supported the mapped transitions, indicating general consistency with canopy greening and canopy decline, supporting the ecological plausibility of the detected changes. This open DEA–ODC workflow provides a transparent, transferable framework for operational wetland monitoring and demonstrates that maintaining natural flood frequency, duration, and connectivity is essential for sustaining the resilience of regulated floodplain systems. Full article

The European Union’s Common Agricultural Policy promotes extensive farming to achieve sustainability goals, yet dairy production in the Baltic states and Poland has continued to intensify, particularly after the abolition of milk quotas in 2015. This study assesses the technical efficiency of intensive and extensive dairy farms in Lithuania, Latvia, Estonia, and Poland over the period 2015–2022, using Data Envelopment Analysis (DEA) combined with a meta-frontier framework that explicitly accounts for technological heterogeneity across production systems. Farms are classified as intensive or extensive based on stocking density relative to forage area, applying the threshold of one livestock unit per hectare. Results show that in all Baltic countries intensive farms exhibit higher meta-frontier technical efficiency than extensive farms, with the gap increasing over time, especially in Lithuania. Technology Gap Ratio results indicate convergence between production systems in Estonia and Latvia, while in Lithuania intensive farms became technologically closer to the national frontier after 2020. In contrast, Poland shows a different pattern: intensive farms operated closer to the meta-frontier but achieved lower efficiency, suggesting managerial constraints. Regression analysis confirmed that production intensity is a positive and statistically significant determinant of meta-frontier technical efficiency in all Baltic countries. These findings suggest that current economic conditions favour intensification and that extensification policies can only be effective if they adequately compensate for the efficiency disadvantage faced by extensive farms. Full article

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

Gait analysis is an essential tool in clinical domains for diagnosing musculoskeletal disorders and evaluating rehabilitation, yet traditional marker-based systems are limited by high costs and spatial constraints. To overcome these challenges, this study proposes and evaluates a joint-level hybrid framework that integrates a single RGB camera with two shoe-mounted inertial measurement units (IMUs) to leverage their complementary strengths. The camera-based module estimates hip and knee sagittal joint angles using 3D pose estimation, where the DEAS optimization algorithm aligns estimated coordinates with a humanoid model, and an LSTM-based refinement network corrects hip angles by referencing more accurately estimated knee data. Simultaneously, the IMU-based module estimates sagittal ankle angles through kinematic chain relationships that combine camera-derived proximal joint information with IMU-measured foot orientation. Experimental validation with 11 healthy participants in a controlled laboratory environment demonstrates promising estimation performance, achieving an average mean absolute error (MAE) of 7.89° and RMSE of 10.09° on the held-out test set across sagittal hip, knee, and ankle angles. Leave-one-subject-out (LOSO) cross-validation of the LSTM correction model further confirmed its generalizability, yielding an average MAE of 6.40° across bilateral hip angles. By accurately mitigating the trunk-inclination-induced overestimation of hip angles with a minimal sensor configuration (one camera and two IMUs), the proposed framework provides a practical and interpretable approach for portable lower limb gait analysis. Full article

The growing environmental crisis, particularly water pollution from detergents, necessitates a shift from reactive compliance to proactive eco-innovation, as current methods often fail to systematically resolve trade-offs between performance, safety, and ecology. This study develops and illustrates the application of the Evolutionary-Driven Design Framework (EDDF), an integrated methodology that combines PESTEL analysis, historical evolutionary pattern analysis, Quality Function Deployment (QFD) with a novel contradiction index, Theory of Inventive Problem Solving (TRIZ), and environmental assessment. The framework was applied to redesign a conventional laundry detergent with the objectives of zero phosphates, superior biodegradability (>85%), maintained efficacy, and controlled cost. The quantitative contradiction index matrix prioritized critical unsustainable parameters (e.g., EDTA, Cocamide DEA) for substitution over mere optimization. Through an iterative feedback loop, the process evolved from a biobased concentrate to an “enzymatic power tablet” (Concept B). This waterless, solid formulation uses sodium citrate as a biodegradable builder and an encapsulated multi-enzyme system, achieving an estimated >90% biodegradability and zero phosphates while meeting technical and economic targets. The EDDF provides a structured, anticipatory roadmap that transforms regulatory and market pressures into drivers of innovation, offering companies a promising method for designing sustainable products by proactively resolving contradictions and avoiding historical mistakes. Full article

Sustainable industrial transition requires productivity evidence that separates real efficiency improvement from scale expansion, capital deepening, and reporting change. This study develops a reproducible frontier-productivity diagnostic for provincial leading industry policy, using official 2013–2023 sector panels for 23 two-digit manufacturing sectors in Shandong Province and a matched 2019–2023 benchmark against Jiangsu. The framework combines input-oriented Banker–Charnes–Cooper (BCC) data envelopment analysis (DEA), Simar–Wilson bootstrap bias correction, Malmquist total factor productivity change (TFPCH) decomposition, producer price index (PPI) deflation diagnostics, scale-productivity classification, and targeted sensitivity tests. Bootstrap correction lowers mean BCC efficiency from 0.77 to 0.69 in Shandong and from 0.79 to 0.70 in Jiangsu. Uniform provincial PPI deflation leaves constant-returns-to-scale (CRS) Malmquist estimates almost unchanged, whereas asymmetric deflation creates measurable sensitivity. Direct sector-cluster resampling places Shandong’s aggregate TFPCH at 1.016 with a 95% interval of 0.995–1.045, supporting a near-stationary interpretation rather than a broad upgrading surge; Jiangsu’s corresponding estimate is 0.976 with a 95% interval of 0.955–0.997. The study does not measure environmental performance directly. It shows how frontier-productivity evidence should be stress-tested and paired with environmental indicators before it is used in sustainability-oriented industrial policy. Full article

Efficiency is a key indicator for evaluating how effectively tourism inputs are converted into outputs. Clarifying the spatial differentiation and driving mechanisms of county-level tourism efficiency can inform regional tourism development and the optimization of resource allocation. Taking counties in Fujian Province, excluding Jinmen County, as the basic unit of analysis, this study constructs a multidimensional input–output indicator system covering tourism, dining, accommodation, transportation, shopping, and entertainment. It applies Data Envelopment Analysis (DEA) to measure county-level tourism efficiency, uses Global Moran’s I and Getis-Ord Gi* hotspot analysis to identify spatial differentiation patterns, and employs GeoDetector to examine key driving factors and their interaction effects. The results show that the average tourism efficiency of county-level units in Fujian is 0.708, indicating a moderate overall level with marked regional polarization. Technical efficiency is relatively high, with an average of 0.873, whereas disparities in scale efficiency represent the main constraint on overall efficiency. Spatially, tourism efficiency displays a pattern of “hot in the north and cold in the south”. Interaction analysis further indicates a shift from resource dependence to service value-added, with dining, entertainment, and shopping exerting stronger effects than tourism resources alone. These findings provide empirical support for optimizing tourism spatial supply and promoting coordinated regional development. Full article

This study implements a Data Envelopment Analysis (DEA) under both input- and output-orientation specifications to measure tourism technical eco-efficiencies and changes in total factor productivity for Eurozone countries from 1996 to 2019. Instead of employing hotel-specific measures or traditional proxies like length of stay or occupancy rate, this study relies on the heterogeneous nature of tourism, namely business and leisure tourism spending, distinguishing between international and domestic visits. Despite their significance for capturing the macroeconomic dynamics of tourism and interactions with the environment, this set of variables is rarely reported in the relevant literature. Efficiency and productivity scores are subsequently examined within a panel regression framework to evaluate the role of renewable energy adoption. The slack analysis reveals input excess and desirable output shortfalls, indicating structural inefficiencies in resource allocation and production performance. Regression findings suggest that the impact of renewables on tourism efficiency and productivity is regime-dependent, while panel causality tests evidence the neutrality hypothesis. The results underscore the need to improve air quality, resource allocation mechanisms, enhance sustainable sector-specific productivity strategies, and accelerate renewable transition policies. Full article

Architectural planning often involves balancing multiple and potentially conflicting objectives, such as safety, economy, functionality, and aesthetics. However, conventional benchmarking approaches typically focus on single performance dimensions and provide limited support for multi-objective decision-making. To address this limitation, this study proposes a benchmark learning framework for multi-objective street house planning that explicitly incorporates architects’ planning preferences. The framework integrates fuzzy sets to define preference functions, indifference curves to represent trade-offs and derive preference weights, and utility functions to quantify satisfaction levels. In addition, Data Envelopment Analysis (DEA) and efficient frontier theory are employed to evaluate planning efficiency and identify optimal benchmark cases. Using empirical data from 627 street houses, the results indicate that the proposed approach effectively captures architects’ subjective preferences while providing an objective assessment of planning efficiency. The integration of indifference curves and the efficient frontier enables explicit visualization of trade-offs, whereas the combination of utility functions and efficiency analysis facilitates the identification of benchmark learning cases. The proposed framework provides a systematic approach to multi-objective optimization in architectural planning by bridging subjective decision-making with quantitative performance evaluation. It offers practical guidance for architects and planners and contributes to the advancement of benchmark-based methodologies in complex design environments. Full article