Search Results (70)

In the context of urban extreme weather events, the efficacy of the “emergency language” employed by governments and public institutions on social media in effectively reaching and guiding the public in a timely manner necessitates a quantifiable evaluation framework. An indicator system was constructed on the basis of Hovland’s persuasion theory. This system comprised five input characteristics (word count/structural clarity, first/second-person perspective, emotional appeal, evidence and framing, and media format) along with three output indicators (reposts, comments, and likes). A data envelopment analysis (DEA) model that is oriented towards output was employed, with disseminators being categorized into four distinct decision-making units: central mainstream media, other government media, local government media, and other media. It is imperative to note that the outputs were subjected to a process of normalization through the implementation of a scale factor. The data were sourced from the Weibo platform within the specified time window, which was from 10:00 on 24 July 2025, to 12:00 on 19 August 2025, with a sample size of 744. The findings revealed substantial disparities in technical efficiency across different disseminator types. A subset of local government media demonstrated a technical efficiency ≈ 1.00 yet low scale efficiency. Posts exhibiting clear structures, actionable points, and accompanying images or videos achieved higher cross-efficiency scores. It is therefore evident that the proposed DEA model provides a benchmark for maximizing dissemination effectiveness under given information characteristics. It is recommended that posting frequencies be maintained at consistent intervals during periods of heightened activity, that a template structure be adopted in accordance with the “fact–action–assistance channel” model, and that the proportion of rich media content be augmented. Full article

The existence of fixed-sum output constraints in real-world situations is widespread, such as market share and carbon dioxide emissions, etc. However, existing fixed-sum output data envelopment analysis (DEA) methods mostly focus on individual decision-making units (DMUs) and ignore the interactions between groups. Therefore, this study first establishes a systematic framework to quantify group performance by the average criterion, and constructs the equilibrium efficient frontier (EEF) to evaluate all groups on a common platform. To address the non-uniqueness issue of EEF, we further introduce the aggressive cross-efficiency mechanism, ultimately proposing a novel group cross-EEF methodology that explicitly accounts for competitive intergroup dynamics. The proposed method is applied in the assessment of carbon emission efficiency in the industrial sector for 30 provinces in China, and the validity of the method is verified. The result shows that (1) even though the average industrial carbon efficiency stands at 1.2015, half of the provinces exhibit values below 1; (2) significant regional heterogeneity is observed, with North China and East China exhibiting higher efficiency levels, while the Northeast and Northwest regions lag behind; (3) provinces such as Beijing, Guangdong, and Zhejiang demonstrate superior performance, in contrast to Ningxia, Hebei, and Qinghai, which remain at relatively low efficiency levels. This study provides theoretical and policy insights to support the advancement of low-carbon development in China’s industrial sector. 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

This study explores the relationship between digital governance, democratic quality, and public funding efficiency across the EU-27, with an emphasis on Greece. Using 2023 cross-sectional data from the DESI, Worldwide Governance Indicators, and Eurostat, we apply OLS regression and simulated DEA to assess how digital maturity and democratic engagement impact fiscal performance. The sample includes all 27 EU member states, and the analysis is subject to limitations due to the cross-sectional design and the use of simulated DEA scores. Results show that higher DESI and Voice and Accountability scores are positively associated with greater efficiency. Greece, while improving, remains below the EU average. The novelty of this paper lies in combining econometric regression with efficiency benchmarking, highlighting the interplay of digital and democratic dimensions in fiscal performance. The findings highlight the importance of integrating digital infrastructure with participatory governance to achieve sustainable public finance. Full article

This study evaluates the operational efficiency of accredited private healthcare facilities in Italy, a sector increasingly complementing the public National Health Service. Unlike previous studies that aggregate public and private providers, this research focuses exclusively on private facilities, providing a consistent and detailed evaluation of their performance. Utilizing game-theoretic cross-efficiency Data Envelopment Analysis (DEA) combined with Classification and Regression Tree (CART) analysis, this study identifies endogenous and exogenous efficiency drivers. Results indicate that private facilities operate at high efficiency levels (mean cross-efficiency = 0.923), with smaller facilities outperforming larger ones, though resources remain underutilized. Inactive ward and bed non-occupancy rates emerge as key inefficiency factors. Regional analysis highlights minimal disparities between the north–center and south, but significant local variations persist, shaped by governance, funding allocation, and institutional frameworks. This study also identifies an “efficiency paradox”, as in deficit regions, private expenditure correlates with higher efficiency, whereas in surplus regions, greater spending does not necessarily improve performance. These findings provide actionable insights for healthcare managers and policymakers, emphasizing the need to maximize capacity utilization, optimize staffing, and structure public–private partnerships strategically. Methodologically, integrating game cross-efficiency DEA with CART strengthens accuracy, offering a robust tool for benchmarking and improving private healthcare performance. Full article

Under the background of global green sustainable development and the urgent need to understand complex regional innovation systems, it is crucial to scientifically assess China’s Tourism Green Innovation Efficiency (TGIE) as a dynamic networked system and reveal its system-level evolution driving mechanism. This article presents the construction of the TGIE evaluation indicator system, measures the inter-provincial TGIE in China in 2011–2023 based on the three-stage super-efficiency SBM-DEA model, analyzes the spatial correlation network characteristics of TGIE by using the motif analysis method and the social network analysis method, and explores the evolutionary driving mechanism by using the time-exponential random graph model (TERGM). The study shows the following: (1) The TGIE of China exhibits a regional distribution pattern characterized by “high in the east and low in the west.” The efficiency of the eastern coastal region is significantly higher than that of the central and western regions, and the overall efficiency shows a fluctuating upward trend. (2) The local structure of China’s TGIE network is dominated by the chain structure, and the partially closed structure is gradually enhanced. It indicates that the bridge role of intermediary nodes in the cross-regional flow of innovation resources is becoming more and more significant. (3) The overall network evolves from a single center to a polycentric collaboration model. High-efficiency regions attract low-efficiency regions to collaborate through high connectivity, and intermediary nodes play a key role in connecting high- and low-efficiency regions. (4) The evolution of China’s TGIE network is driven by both exogenous and endogenous dynamics, showing significant path dependence and path creation characteristics. This study enhances the theoretical framework of complex systems in tourism innovation and offers theoretical support and policy insights for optimizing the network structure of China’s TGIE as a complex adaptive system and maximizing regional cooperation networks. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Drag embedment anchors (DEAs) play a vital role in maintaining the stability and safety of offshore structures, including floating wind turbines, oil rigs, and marine renewable energy systems. Accurate prediction of anchor performance is essential for optimizing mooring system designs, reducing costs, and minimizing risks in challenging marine environments. By leveraging advanced machine learning techniques, this research provides innovative solutions to longstanding challenges in geotechnical engineering, paving the way for more efficient and reliable offshore operations. The findings contribute significantly to developing sustainable marine infrastructure while addressing the growing global demand for renewable energy solutions in coastal and deep-water environments. This current study evaluated tree-based machine learning algorithms, e.g., decision tree regression (DTR) and random forest regression (RFR), to predict the holding capacity and efficiency of DEAs in sand seabed. To train and validate the results of machine learning models, the K-fold cross-validation method, with K = 5, was utilized. Eleven geotechnical and geometric parameters, including sand friction angle (φ), fluke-shank angle (α), and anchor dimensions, were analyzed using 23 model configurations. Results demonstrated that RFR outperformed DTR, achieving the highest accuracy for capacity prediction (R = 0.985, RMSE = 344.577 KN) and for efficiency (R = 0.977, RMSE = 0.821 KN). Key findings revealed that soil strength dominated capacity, while fluke-shank angle critically influenced efficiency. Single-parameter models failed to capture complex soil-anchor interactions, underscoring the necessity of multivariate analysis. The ensemble approach of RFR provided superior generalization across diverse seabed conditions, maintaining errors within ±10% for capacity and ±5% for efficiency. Full article

The present study examines the impact of customer firms’ environmental, social, and governance (ESG) performance on suppliers’ green innovation efficiency, grounded in stakeholder theory and innovation diffusion theory. The DEA-SBM model is employed to measure green innovation efficiency and analyze transmission mechanisms through knowledge spillovers, financing constraints, and the moderating roles of executives’ green cognition and digitization. This analysis is based on panel data from 3134 customer–supplier pairs of China’s A-share listed firms from 2014 to 2023. The findings indicate that high ESG performance by customer firms has a substantial impact on suppliers’ green innovation efficiency, with a 1% increase in customer ESG score resulting in a 1.38% improvement in supplier efficiency. The phenomenon under scrutiny is hypothesized to be precipitated by knowledge spillovers and mitigated by reduced financing constraints. The hypothesis further posits that supplier firm executives’ green cognition and customer digitization will amplify the effect. A heterogeneity analysis reveals stronger effects in technology-intensive firms and regions with higher governmental environmental oversight. These findings underscore the pivotal function of ESG-driven supply chain collaboration in propelling sustainable industrialization. It is imperative that policymakers prioritize cross-regional ESG benchmarking and digital infrastructure to amplify green spillovers. Conversely, firms must integrate ESG metrics into supplier evaluation systems and foster executive training on sustainability. This research provides empirical evidence for the optimization of green innovation policies and the achievement of China’s dual carbon goals through the coordination of supply chain governance. Full article

Renewable energy (RE) is pivotal to achieving both environmental sustainability and long-term energy security, yet systematic evidence on the efficiency of RE investment across South and Southeast Asia remains sparse. This study introduces a rejoice–regret utility cross-efficiency DEA (RRUCE-DEA) framework that fuses conventional quantitative efficiency measurement with the behavioral insights of regret theory. Applying the model to 16 countries shows India as the benchmark for efficient RE investment allocation, followed closely by Pakistan and Indonesia. The Philippines, Malaysia, and Vietnam also post strong results, whereas Sri Lanka and Thailand reveal moderate performance with clear room for improvement. At the lower end of the spectrum, Cambodia, Myanmar, and Afghanistan encounter significant hurdles that must be overcome to achieve a successful clean energy transition. A sensitivity analysis further explores how variations in the regret aversion and rejoice–regret coefficients affect the RRUCE-DEA outcomes. The findings provide actionable guidance for policymakers and investors seeking to channel resources toward a cleaner, more sustainable regional energy portfolio. Full article

Efficient urban water management is crucial for sustainability, especially in contexts such as Mexico. Therefore, assessing the performance of Water Utility Organizations (WUOs) is very important. This study assesses the technical efficiency of 49 Mexican WUOs using cross-sectional data for 2020 and investigates the effect of geographic location as a potential determinant. A two-stage approach was applied. First, Data Envelopment Analysis (DEA) oriented to inputs (under Constant (CRS) and Variable (VRS) Returns to Scale assumptions) was used to evaluate technical efficiency with input measures of employment and costs, and output measures of volume produced and population served. The second stage involved Tobit regression modeling to examine the determinants of technical inefficiency derived from the DEA (censored left at zero), testing the effect of geographic microregions. The DEA results presented a rather significant average inefficiency (mean scores of 0.73 CRS, 0.82 VRS), which implies input savings of 18–27% could still be in the shelves. Notably, the subsequent Tobit modeling found that wide geographical microregions were not statistically significant (p > 0.79) in accounting for those inefficiencies, implying zero explanatory power. The findings indicate that improvements in efficiency require going beyond broad geography to probably focus on local managerial, institutional, or operational considerations. The present study provides empirical benchmarks for Mexican WUOs and evidence on the limited role of broad geography, thereafter directing future research toward specific performance determinants. Full article

Offshore wind energy has become a critical component of global efforts to transition toward low-carbon and sustainable energy systems, and although Taiwan’s advantageous geographical position has accelerated its progress in this domain, many of Taiwan’s upcoming offshore wind projects remain in a pre-construction phase, raising questions about their viability for complementary wave energy integration. To address this challenge, this study proposes a hybrid Cross-Efficiency Slacks-Based Measure (CE-SBM) Data Envelopment Analysis (DEA) model. Thirteen announced offshore wind projects were evaluated using spatial and resource-related input variables and energy-centric output variables. The self-efficiency results from the SBM stage highlighted several projects—most notably Zhu Ting, Wo Neng, and Chu Tin—as highly effective in resource utilization under their own weighting schemes. However, the subsequent cross-efficiency analysis added a consensus-based perspective, revealing a clear performance hierarchy and identifying inefficiencies in projects such as Greater Changhua Northeast and Winds of September. These findings underscore the value of combining DEA-based models with slacks-based and cross-efficiency features to guide multifaceted energy development. By prioritizing projects with robust efficiency profiles, policymakers and stakeholders can expedite Taiwan’s broader adoption of integrated wind–wave energy systems, ultimately fostering a more reliable and sustainable marine energy portfolio. Full article

Against the backdrop of global challenges to food security and China’s push to modernize its agriculture, it is critical to understand how government strategies affect innovation efficiency. This study examines how three modes of intergovernmental competition—fiscal spending competition (strategically increasing public spending to attract resources), tax competition (providing incentives to promote investment), and promotion competition (officials prioritizing short-term projects for promotion)—affect the efficiency of agricultural science and technology innovations across China’s provinces. Utilizing panel data (2000–2021) and a Slack-Based Measure Data Envelopment Analysis (DEA-SBM) model, we find that fiscal spending competition suppresses efficiency, particularly in western regions where infrastructure investments crowd out R&D. Tax competition enhances efficiency, yet its impact is attenuated in central China due to low industrial upgrading. Promotion competition impedes long-term innovation, as frequent official turnover prioritizes short-term projects. Regional heterogeneity highlights eastern China’s market-driven advantages versus central/western regions’ structural constraints. Policy implications advocate for spatially differentiated governance, including R&D tax rebates in the east and cross-regional innovation alliances. This study contributes to fiscal decentralization theory by revealing the nonlinear effects of competition modes on agricultural innovation. Full article

The digital economy (DE) is characterized by invention, low energy consumption, cross-sector integration, and open sharing. It can effectively enhance social production methods, influence consumer behavior, and provide new pathways to enhance total factor energy efficiency (TFEE). This paper studies 280 Chinese cities, employing the entropy method and data envelopment analysis (DEA) model to evaluate and analyze urban DE and TFEE. It also constructs a system generalized method of moments model (SGMM model) and a threshold regression model (TR model) to examine the impact of the DE on TFEE in China. The main study findings include the following: (1) The regression results of the SGMM model indicate that the effect of DE on TFEE in Chinese cities shows a U-shaped trend. (2) The regression results of the TR model further confirm a U-shaped association connecting DE and TFEE, with the threshold estimated at 0.304. (3) The economic factors and industrial structure have a major impact on inhibiting the improvement of TFEE, whereas technological advancements and environmental regulations significantly facilitate its improvement. Full article

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts. Full article