Search Results (1,134)

This study investigates whether holder-side RMB-related exposure is systematically associated with macroeconomic performance efficiency. Using a multi-objective efficiency framework rather than single macroeconomic indicators, we construct economy-year efficiency scores for 25 economies over 2005–2018 based on data envelopment analysis (DEA) and cross-efficiency evaluation. We then examine how these scores vary with RMB-related exchange-rate exposure and China-related value-added linkage proxies, while distinguishing RMB-specific exposure from broader trade integration and structural conditions. The results suggest conditional associations between holder-side RMB-related exposure and macroeconomic performance efficiency. The exchange-rate channel is positive and statistically significant under the baseline DEA specification (p < 0.01) when using PCSE. The estimated magnitude is economically modest and sensitive to alternative ICT proxies, efficiency benchmarks, and lag structures. By contrast, the GVC channel provides suggestive rather than confirmatory evidence, as China-related value-added linkages are not robustly significant under the revised fixed-effects specifications, augmented controls, or two-way fixed effects. The positive ERPT association is consistent in sign across the pooled CCR and genuine Game Cross-efficiency benchmarks. The VRS/BCC results are used as a complementary first-stage sensitivity check on the returns-to-scale assumption. The magnitude and statistical inference remain sensitive to alternative specifications and variance estimators. Overall, the findings should be interpreted as reduced-form, mechanism-consistent associations rather than causal effects of RMB internationalization. Full article

Background/Objectives: Health systems involve numerous stakeholders interconnected through nonlinear relationships. While Data Envelopment Analysis (DEA) has been widely used to measure health system efficiency, conventional estimates may exhibit finite-sample bias. An important question, therefore, concerns how health system performance can be measured more reliably, and what factors explain cross-country differences in efficiency. This study introduces an integrated framework that combines Two-Stage Bootstrap DEA with machine learning to assess the performance of the health systems of 26 OECD countries using 2022 data. Methods: In the first step, technical efficiency scores are computed using an output-oriented constant returns to scale (CRS) DEA model. Subsequently, bias-corrected efficiency estimates are derived using the Bootstrap procedure proposed by Simar and Wilson. In the second step, truncated regression analysis and machine learning-based partial dependence analysis, the latter validated through leave-one-out cross-validation, are employed to investigate the determinants of efficiency. Results: The Bootstrap procedure reveals statistically significant differences from conventional DEA results, and bias-corrected results indicate that South Korea, Canada, and the United States achieve the highest efficiency levels. The findings show that tobacco use prevalence has a significantly negative association with health system efficiency and alcohol consumption exhibits a negative, threshold-type pattern, while GDP per capita and out-of-pocket health expenditure display more complex, nonlinear effects. Furthermore, the scenario analysis indicates that a 10% reduction in tobacco use yields the largest predicted single-intervention improvement, while combined interventions produce additional but sub-additive gains. Conclusions: The proposed framework presents a transparent and validated approach for assessing and explaining health system performance, generating findings relevant to the development of evidence-based health policy. Full article

Rainfed wheat covers much of the semi-arid Mediterranean, yet its environmental cost is rarely measured at the field scale where management decisions are made. This study assesses the carbon and environmental footprint of rainfed wheat in the Mygdonia Basin, Northern Greece, drawing on a primary dataset of 1385 fields (~976 ha). Life Cycle Assessment (LCA) was coupled with Data Envelopment Analysis (DEA) to link environmental performance with technical efficiency. Carbon footprint ranged from 2030 to 3110 kg CO₂e ha⁻¹, revealing pronounced spatial variability across management systems. Excess nitrogen fertilization emerged as the principal driver of greenhouse gas emissions, acidification, and eutrophication in lowland systems. High-altitude systems showed lower impacts per hectare, an advantage partly offset by higher terrestrial ecotoxicity from increased machinery use. DEA revealed widespread technical inefficiency, most fields scoring between 0.70 and 0.90, with input slacks tracing this to excess nitrogen and seed. The convergence of LCA and DEA findings identifies nitrogen management as the key leverage point for improving environmental and technical performance without compromising productivity. The findings support spatially differentiated interventions under the Common Agricultural Policy, promoting precision nutrient management and conservation-oriented practices to strengthen the resilience of Mediterranean agroecosystems. Full article

Reducing poverty and income inequality is a major objective of contemporary social policy. Considerable financial resources are spent on social protection to support this purpose. Since these resources are limited, their efficient use is important for improving poverty and income inequality outcomes. The aim of this study is to assess the efficiency and productivity of social protection expenditure in eleven Central and Eastern European (CEE) EU Member States through Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). Three input-oriented DEA models are estimated for 2016–2023, including two poverty-oriented models and one income-distribution-oriented model. The results indicate high efficiency and scale efficiency across countries, but favourable results in both poverty-oriented models are not necessarily accompanied by similar results in the income-distribution-oriented model. MPI decomposition shows that productivity dynamics in the poverty-oriented models reflect mainly technical efficiency changes before the COVID-19 pandemic and technological change thereafter, whereas productivity dynamics in the income-distribution-oriented model reflect mainly technical efficiency changes throughout the period. Full article

In DEA cross-efficiency, information aggregation of self-evaluation and peer-evaluation has been one of the main concerns. The inherent variability of decision-makers’ preferences in peer-evaluation, whether expressed aggressively or benevolently within the secondary goal programming, can yield divergent peer-efficiency scores. Therefore, interval efficiency for considering different preferences of decision-makers and group opinions is used to represent the variable efficiency range. In this paper, comparison norms for the advantage degree between any two interval efficiencies are defined, and a pairwise advantage-degree matrix is developed. By aggregating this matrix through a dominance score, a unique ranking of DMUs can be obtained. Subsequently, a synthetic evaluation model, ICE-AB, is proposed for DEA cross-efficiency evaluation. To demonstrate the effectiveness, discriminatory capability, and practical applicability of ICE-AB, a benchmark numerical example, a differentiating numerical case, and an empirical application to major Chinese new energy vehicle manufacturers are provided. Comparative analysis and ranking results show that the new model not only preserves different peer-evaluation information but also obtains the unique ranking results of DMUs under uncertain attitudes of decision-makers. Full article

Reasonable carbon allowance allocation for power transmission and transformation projects is critical for regional emission reduction, yet existing methods rarely address spatial heterogeneity or the fairness-efficiency trade-off at the city level. This study proposes a framework integrating entropy weighting with Zero-Sum Gains Data Envelopment Analysis (ZSG-DEA), applied to cities in Liaoning, China, to achieve objective initial distribution and efficiency-driven adjustments under a binding carbon cap. Empirical results reveal that regional quota levels are shaped not by economic scale or historical emissions alone, but by grid hub functions, load intensity, new energy transmission demand, and network density. The integrated approach avoids subjective bias and optimizes allocation efficiency, offering a replicable pathway for carbon quota allocation in similar contexts. Full article

Objectives: Hospital efficiency provides a systems-level perspective for identifying unrealized capacity to improve care delivery, quality, and patient safety. This study evaluated relative technical efficiency among U.S. short-term acute care hospitals (2018–2024) and evaluated how structural IT operating investments are converted into clinical service volume and quality-related performance. Methods: A longitudinal panel of 8589 hospital-year observations was utilized to estimate technical efficiency with an output-oriented Data Envelopment Analysis (DEA) under variable returns to scale. A secondary Simar–Wilson double-bootstrapped truncated regression (n = 2147 complete casesexamined associations with bias-corrected efficiency, clinical quality, case mix, and operational scale. Results: Mean DEA efficiency was 0.567, with 7.38% (n = 634) operating on the annual efficiency frontier and a mean output expansion potential of 102.49%. Efficient hospitals maintained higher IT operating spending. Stage 2 regression showed that higher Hospital Value-Based Purchasing quality performance was significantly associated with greater inefficiency. Conversely, higher case mix complexity and larger operational scale were associated with higher efficiency. Conclusions: Most hospitals operated below the best-practice frontier, indicating gaps in converting resources into service volume and quality. Because core operational drivers were included in the primary DEA model, observed associations show descriptive structural patterns rather than direct cause-and-effect relationships. Full article

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice. Full article

Moderate-scale farming is an important pathway for promoting the high-quality development of China’s pig industry. Based on survey data from 809 pig farms in Heilongjiang Province in 2024, this study assesses the moderate operating scale of pig farming within the framework of herd management and disease control, focusing on production efficiency, disease prevention and control, and environmental management. Data Envelopment Analysis (DEA) is used to measure production efficiency, and baseline regression models with operating-scale group dummy variables are constructed to compare operational performance across annual slaughter-volume groups. Compared with the other operating-scale groups, relatively better production efficiency was observed in the 5000–10,000 head group. The 3000–4999 head group shows relatively better disease prevention and control performance, with lower pig mortality, and a greater advantage in controlling manure treatment costs. Overall, the relatively better-performing scale range differs across evaluation dimensions. However, the 3000–4999 head group shows relatively stronger coordination among production organization, disease prevention and control, and environmental management, and may therefore represent a relatively suitable operating-scale range for supporting herd management and disease control in pig farming. Full article

This study examines the technical and operational efficiency of publicly listed energy companies operating in Europe, the United States, and Canada during the 2017–2024 energy transition period. The sample includes both traditional electricity utilities and renewable energy producers. Technical efficiency was estimated using output-oriented Data Envelopment Analysis (DEA), specifically the Charnes–Cooper–Rhodes (CCR) and Banker–Charnes–Cooper (BCC) models. Panel-data models were subsequently applied to identify the financial, organisational, regional, and environmental, social, and governance (ESG) factors associated with firm-level efficiency. The results indicate a moderate average level of technical efficiency, with a substantial share of inefficiency attributable to an inappropriate operating scale. Contrary to the initial hypothesis, renewable energy companies were, on average, less technically efficient than traditional utilities, despite achieving higher ESG and environmental scores. European companies exhibited higher efficiency than firms located in the United States and Canada, suggesting that long-term exposure to climate-policy and regulatory pressures may encourage more effective resource use. The panel-model results did not provide robust evidence that ESG performance directly improves technical efficiency. By contrast, profitability, leverage, and firm size were significantly associated with efficiency outcomes. These findings show that the energy transition depends on more than the expansion of renewable energy capacity. Effective resource allocation, financial resilience, organisational adjustment, and an appropriate operating scale are equally important. The study provides relevant implications for corporate managers, investors, and policymakers involved in energy-sector transformation. Full article

In most production systems, the objective is to minimize input consumption while maximizing the generation of desirable outputs. However, many production processes also generate undesirable outputs as by-products of desirable outputs. In many real-world systems, undesirable outputs are inherently linked to the production of desirable outputs. Several studies in the Data Envelopment Analysis (DEA) literature have addressed performance evaluation of decision-making units (DMUs) in the presence of undesirable outputs. However, most existing models assume that desirable and undesirable outputs are independent, which may not reflect real production environments. The objective of this study is to model the dependency between desirable and undesirable outputs and to develop a novel DEA framework based on an axiomatic approach. Specifically, the classical axiom of output disposability is decomposed into two separate axioms: disposability of desirable outputs and disposability of undesirable outputs. Based on these axioms, a new production possibility set (PPS) is constructed. The proposed DEA model explicitly incorporates the dependency between desirable and undesirable outputs. A case study involving sensor monitoring systems is presented to demonstrate the applicability of the proposed approach. Full article

Rigid polyurethane foams are used in impact-attenuation systems due to their tunable cellular structure and energy dissipation capacity. However, expanded polystyrene (EPS), commonly used for impact protection, presents limitations related to impact attenuation performance and limited design flexibility. This study evaluates the impact performance of rigid polyurethane foams modified with diethanolamine and assesses formulation efficiency using Data Envelopment Analysis (DEA). Rigid PU foam formulations containing 0–3 wt% DEOA were synthesized and characterized by impact testing, apparent density measurements, Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, and Thermogravimetric Analysis/Derivative Thermogravimetry. DEA was applied to correlate diethanolamine content with impact absorption efficiency. Excessive crosslinking and reduced energy dissipation were observed above 2 wt%, while concentrations below 0.5 wt% resulted in poorly structured foams. The formulation containing 1 wt% DEOA was identified as the most efficient among the investigated formulations, exhibiting the best overall performance, reducing transmitted peak acceleration by 13.8% compared with neat PU foam, while exhibiting an approximately 48% increase in apparent density, more complete consumption of NCO groups, a more uniform cellular structure, and only modest changes in thermal degradation behavior. These findings indicate that the improved impact performance is associated with the combined effects of increased apparent density, modified cellular morphology, and changes in the polyurethane network promoted by DEOA, underscore the promise of diethanolamine-modified rigid polyurethane (PU) foams for protective applications. Full article

Water pumping stations are critical components of water transmission systems, particularly in the Gulf region, where potable water supply is heavily dependent on energy-intensive desalination processes. Despite their importance, pumping station efficiency is often assessed using single-dimensional indicators that fail to capture operational complexity, scale effects, and environmental impacts. This study develops a Data Envelopment Analysis (DEA) framework to evaluate the performance of 16 water pumping stations in Dubai, each treated as a decision-making unit (DMU), by incorporating both operational and sustainability dimensions. A multi-model approach was applied, integrating input-oriented Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models with Slack-Based Measure (SBM) models that treat energy consumption as an undesirable output. The results reveal substantial variation in efficiency, with average scores of 0.73 under CCR, 0.95 under BCC, 0.62 under SBM Constant Returns to Scale (CRS), and 0.86 under SBM Variable Returns to Scale (VRS). The gap between CCR and BCC results indicates that inefficiencies are primarily driven by scale rather than managerial performance, while the lower SBM scores highlight the significant impact of energy consumption on overall efficiency. Peer analysis confirms the robustness of the findings, identifying Decision-Making Unit (DMU) 16 as the primary benchmark, appearing 37 times across the models. Slack analysis further reveals critical inefficiencies, particularly in DMU 03 and DMU 06. Full article

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

This work presents an extension of the Belgrade ACol collisional database within the Virtual Atomic and Molecular Data Centre (VAMDC) framework to include dissociative electron attachment (DEA) processes. DEA, a low-energy electron-driven resonant mechanism leading to molecular fragmentation, is relevant in fields such as plasma science, radiation damage, nanofabrication, and EUV lithography. The ACol data model was redesigned to improve semantic clarity and flexibility by separating physical collision data from bibliographic and serialization structures. A new DataSource entity and a redefined TabulatedData–Collision relationship enable a more normalized database while preserving compatibility with the XSAMS schema through dynamic, query-time construction of hierarchical structures. The implementation is demonstrated using DEA to isoflurane, incorporating experimental data from independent studies and cataloging resulting fragment anions and energy-dependent yields. Elastic cross sections of isoflurane are included as they are immanently connected to DEA, providing an essential tool for understanding the complex DEA process. The novelty of the present implementation lies not in the first representation of DEA within VAMDC, but in its integration into a multi-process collision database based on a compact, normalized data model that separates scientific data and bibliographic provenance from the XSAMS serialization structure. The updated architecture enhances interoperability, reduces redundancy, and improves data management while maintaining compliance with VAMDC standards. Full article