Search Results (582)

Aligned with Sustainable Development Goal 4, the efficient and sustainable allocation of educational resources is essential for improving the quality of higher education. This study investigates the paradox between resource investment and the operational efficiency of Chinese key universities and explores pathways for sustainable resource allocation in higher education. Employing a two-stage Data Envelopment Analysis (DEA) and Bootstrap truncated regression, we evaluated the operational efficiency of 60 universities directly affiliated with the Ministry of Education of China from 2004 to 2023 and further analyzed factors influencing efficiency. The results show that greater resource concentration does not necessarily improve operational efficiency and may generate diminishing returns in resource use. Efficiency differences across university groups also vary with model specification and output composition, especially in research performance and doctoral education. Further analysis shows that faculty structure optimization, high-quality postgraduate education, and stable teaching teams are pivotal factors in enhancing universities’ sustainable operational efficiency. These findings highlight the importance of governance reform and performance-oriented resource allocation in supporting the sustainable development of higher education systems and the achievement of the Sustainable Development Goals. Full article

This study evaluates the productive efficiency in the agrifood sector of 21 rural Bulgarian districts as a proxy for territorial competitiveness. Output-oriented Data Envelopment Analysis (DEA) was performed using district-level data from 2022 to 2024. The analysis incorporates five inputs related to labor, land, and capital and three economic outputs from agriculture and food processing. Results indicate substantial variation in efficiency among rural districts. Twelve districts form the efficiency frontier, with effective resource use and diverse structures; nine are inefficient due to scale or organizational/technological constraints. Bootstrap bias correction revealed standard DEA underestimates efficiency gaps. Frontier districts include large plains, mountainous regions and smaller, specialized systems, indicating diverse paths to competitiveness. A composite Territorial Competitiveness Index (TCI) showed frontier status does not guarantee efficiency, often due to underused manufacturing capital. Cluster analysis identified four performance groups needing different policy support, ranging from near-frontier territories that need knowledge transfer to deeply underperforming districts that require restructuring. No geographic clustering of efficiency was found, pointing to structural and institutional, rather than geographic, drivers. These results highlight the need for territorially tailored rural policies within the Common Agricultural Policy (CAP) and offer an empirical basis for diagnosing regional agrifood efficiency gaps. Full article

Green financial inputs (GFI) play an important role in promoting green innovation in the manufacturing industry, and accurately evaluating GFI utilization efficiency and its industry heterogeneity is crucial for optimizing green resource allocation. To address this, this study applies a three-stage Data Envelopment Analysis (DEA) model, using panel data of 29 Chinese manufacturing sectors from 2011 to 2024. This model eliminates the interference of environmental factors and statistical noise via the Stochastic Frontier Analysis (SFA) in the second stage, thus obtaining more reliable efficiency evaluation results. The empirical results show that: (1) GFI can effectively improve manufacturing green innovation efficiency (GIE), but the overall utilization efficiency remains at a low level; (2) there exists significant industry heterogeneity, with technology-intensive industries performing best in GFI utilization efficiency, followed by capital-intensive industries, and labor-intensive industries the worst; (3) environmental regulation and green financial market environment significantly improve GFI utilization efficiency, while government green finance support and market structure have no significant effects on it; (4) after eliminating external disturbances, the real GFI utilization efficiency tends to be stable, and the efficiency decline in 2023–2024 is mainly caused by external shocks. Corresponding targeted implications are put forward to optimize GFI allocation and promote balanced green development of China’s manufacturing industry. Full article

Global environmental issues are becoming increasingly severe, with climate change imposing varying degrees of economic impact on different cities. It is crucial for cities to pursue efficient, low-carbon, and sustainable development pathways to cope with climate change. Carbon emission efficiency (CEE) is an essential indicator for assessing their performance and progress toward low-carbon growth. However, traditional CEE assessments have yet to integrate regional differences in the socioeconomic costs of climate change. To fill this gap, we have built a combined efficient frontier Data Envelopment Analysis (DEA) model based on the weighted carbon emissions of each city’s climate costs to evaluate the CEEs of 252 cities in China from 2006 to 2021. Meanwhile, city classification and spatial Markov chains are used for spatio-temporal heterogeneity analysis, and finally, the efficiency is decomposed to determine the impact of different factors on carbon efficiency. The results indicate that the average CEE of coastal cities (0.57) is lower than that of inland cities (0.63), mainly due to higher climate costs and unbalanced development. In contrast, megacities and super-large cities in coastal areas have the highest CEE levels because of economies of scale and technological advantages. Efficiency decomposition shows that pure technical efficiency (PTE) is the primary driver of CEE differences, contributing 33.37% to inefficiency differences. Our findings emphasize the need for targeted, differentiated policies to address unique urban challenges. Green technology investments should be prioritized in areas with high emission reduction potential, while cross-regional technology diffusion mechanisms should be established in areas with medium reduction potential to foster innovation. Overall, this study could offer valuable insights into the sustainable and low-carbon transition of urban development. Full article

Sustainable low-carbon logistics serves as a key driver for economic development in China’s eastern coastal regions. This study evaluates the efficiency of low-carbon logistics across 12 provinces from 2013 to 2022, incorporating both environmental and economic dimensions. The analysis begins with Pearson’s correlation tests to examine relationships between input and output variables, followed by a three-stage Data Envelopment Analysis (DEA) model to compute efficiency scores. After adjustment, overall comprehensive technical efficiency slightly declined from 0.811 to 0.799, while pure technical efficiency improved from 0.919 to 0.931 and scale efficiency decreased from 0.885 to 0.859. Provinces such as Hebei and Liaoning demonstrate high and stable development, whereas Beijing and Hainan are constrained by declining scale efficiency. Expanding the research scope from individual provinces to the entire eastern coastal region, this study combines a three-stage DEA model with the Malmquist index to provide both static and dynamic analysis. A scientifically constructed indicator system incorporates carbon emissions, highlighting the synergy between economic and environmental performance. A key finding is the identification of scale diseconomies as a significant constraint on regional low-carbon logistics efficiency. The results suggest that policymakers should adopt tailored strategies, prioritize targeted environmental investments, and enhance cross-regional collaboration. For corporate managers, we emphasize shifting from scale-driven expansion to technology-enabled refinement, with a focus on advancing precision in operations. These insights offer a valuable reference for promoting sustainable, high-quality, and low-carbon logistics development in other regions. Full article

This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation that preserves linearity and avoids probabilistic assumptions. Robustness is interpreted in the pessimistic interval DEA sense, where efficiency is evaluated under worst-case realizations of observed bounds rather than through explicit uncertainty-set optimization. To mitigate weight degeneracy and enhance discrimination power, data-driven proportional weight restrictions are introduced; these endogenous bounds are constructed solely from observed data and regularize the multiplier space without relying on subjective preferences or tuning parameters, while maintaining scale invariance and the nonparametric nature of DEA. The model admits equivalent multiplier and envelopment formulations and enables meaningful decomposition of overall efficiency into stage-specific components. Fundamental theoretical properties—including feasibility, boundedness, monotonicity, efficiency decomposition, and special case consistency—are rigorously established. An empirical application to OECD macroeconomic data, accompanied by sensitivity evaluation, demonstrates the stability and discriminatory capability of the proposed framework under bounded variability. Computational analysis confirms that the model retains linear programming structure and exhibits linear growth in problem size with respect to the number of decision-making units, thereby preserving the scalability characteristics of classical two-stage network DEA formulations. The proposed framework provides a theoretically grounded and computationally tractable approach for network efficiency analysis under bounded interval uncertainty. Full article

Against the backdrop of sustainable development and global climate governance, this study focuses on the evaluation and trend prediction of provincial carbon emission efficiency and constructs a multi-model integrated analytical framework featuring “data preprocessing—efficiency decomposition—dynamic forecasting—policy deduction”. First, economic, energy consumption and carbon emission data for 30 provinces in China from 2009 to 2019 are collected. Data cleaning is performed through outlier identification and Lagrange interpolation, and a cross-regionally comparable quantification system is established based on a unified carbon emission standard, laying a foundation for subsequent analysis. Second, data envelopment analysis (DEA) is adopted to decompose carbon emission efficiency. It is found that approximately 23% of provinces lie on the technical efficiency frontier, with the average variance share of technical inefficiency being 0.62; 6% of provinces have the potential for scale expansion; and 10% suffer from diseconomies of scale, reflecting significant structural efficiency losses in regions concentrated with high-carbon industries. Third, the long short-term memory (LSTM) neural network is employed for dynamic forecasting and scenario simulation of carbon emissions by 2025. The model’s prediction error in 2019 is controlled within 8.7%. Simulation results show that when the share of clean energy rises to 35%, China’s national carbon emission growth rate can be reduced to 1.2% by 2025. However, multi-scenario sensitivity analysis indicates that the achievement of this target highly depends on policy enforcement intensity and power grid accommodation capacity. In addition, stochastic frontier analysis (SFA) reveals the heterogeneous contributions of different energy types to economic and social outputs. The consumption elasticities of electricity, liquefied petroleum gas and gasoline are significantly positive, whereas the negative elasticities of oil, fuel oil and coal deeply reflect the low energy utilization efficiency and rigid lock-in of high-carbon industries in some regions. Finally, combined with efficiency evaluation, trend prediction and mechanism analysis, differentiated emission reduction strategies are proposed for technologically backward provinces, scale-imbalanced provinces and clean energy base provinces, forming a complete closed loop from “efficiency diagnosis” to “future deduction” and then to “policy feedback”. This study breaks through the limitations of a single model. Through the coupling of parametric and non-parametric methods, as well as the integration of dynamic forecasting and scenario simulation, it effectively addresses issues such as data heterogeneity. It provides scientific support for local governments to formulate emission reduction policies and optimize energy structures, establishes a methodological foundation for industrial efficiency analysis and international carbon responsibility allocation research, and helps to promote regional clean, low-carbon, and sustainable development. Full article

Urban innovation capacity is increasingly critical to city development, and quantitative assessments of innovative cities’ innovation capability can be achieved via the composite index method, which fully integrates multidimensional indicators. This study develops a hierarchical data envelopment analysis (H-DEA) method to establish a composite index evaluation model for innovation capacity, which features flexible and objective two-level indicators—an advantage that avoids subjective weight assignment and adapts well to the hierarchical structure of innovation evaluation indicators. The proposed H-DEA model is applied to evaluate 67 innovative cities in China, yielding composite scores and rankings that are further compared with those from the traditional weighting method. Sensitivity analysis is conducted by adjusting different upper and lower bounds of the H-DEA model to verify its robustness. Additionally, these 67 cities are divided into four regions, with region-specific weights assigned to the evaluation indicators in the model. The results show that the eastern region has the highest average innovation capacity (0.3783), where technological innovation (weight 0.27) serves as a key driving force; the western region has the lowest average innovation capacity (0.3235), and its innovative cities should prioritize improving outcome transformation capacity (weight 0.1357). Overall, technological innovation receives the highest average weight (0.2422), while outcome transformation capacity gets the lowest (0.1647). Full article

Underground tunnel construction projects using tunnel boring machines (TBMs) require a holistic risk perspective. Such projects face various risks arising from social, economic, political, workforce, and regulatory aspects during project execution. It is necessary to develop preventive strategies for managing these risks and thereby ensure timely project delivery, cost efficiency, and safety. In this study, we aimed to develop a comprehensive hybrid decision-making framework for analyzing risks in TBM-based tunnel construction projects. The proposed approach integrates the best–worst method (BWM), data envelopment analysis (DEA) model-based risk assessment, and the preference ranking organization method for enrichment evaluation (PROMETHEE). The BWM was applied to determine the weights of decision criteria with fewer comparisons and improved consistency. Subsequently, the DEA model was then used to compute local risk scores under multiple input and output conditions. Finally, PROMETHEE was employed to analyze the risks based on positive and negative outranking flows. The proposed approach was applied to a realistic metro construction project in Bangkok. The findings indicated that the proposed approach effectively compromised all the decision-making attributes to manage the uncertainties. The proposed methodology can support project managers, stakeholders, engineers, and relevant authorities in identifying high-priority risks and implementing effective mitigation strategies to enhance risk management in tunnel construction. Full article

In Morocco, the increasing public expenditure on essential sectors, such as education, does not always lead to improved outcomes, highlighting a significant gap between resource allocation and quality enhancement. This study examines the efficiency of public expenditure in education, health, and infrastructure from 1990 to 2022, employing a robust Data Envelopment Analysis (DEA) approach supplemented by bootstrap regression techniques. Our analysis reveals considerable inefficiencies, particularly in education, where higher expenditures have not consistently resulted in greater efficiency. This underscores the importance of prioritising quality, effective management, and optimal resource utilisation alongside budget increases. By integrating DEA with bootstrap methods, we provide more reliable efficiency estimates and identify key economic factors, such as inflation, urbanisation, corruption, and political stability that influence the performance of public expenditure. These findings offer valuable insights for policymakers aiming to optimise resource use and enhance the effectiveness of public expenditure within Morocco’s broader development strategy. Full article

The present study evaluates the performance of hotel companies in Romania using Data Envelopment Analysis (DEA) integrated with a hybrid weighted TOPSIS model (Technique for Order Preference by Similarity to the Ideal Solution). This approach captures both technical efficiency and multidimensional competitiveness. The DEA included an output-oriented Variable Returns to Scale (VRS) model (with four inputs and one output). It was followed by TOPSIS aggregation with hybrid entropy weights to obtain a composite performance index. The research used cross-sectional financial data for 2023, specific to hotels in Romania, and allowed interpretation across five territorial categories based on predominant relief. The results show that the 852 analyzed hotels have a relatively homogeneous structure and moderate variations in performance scores. At the same time, top-performing units are strongly concentrated in economically or touristically dynamic counties. The integrated DEA–TOPSIS results indicate that high-performing hotels tend to cluster spatially, with plain counties hosting the largest number of hotels at the national level and also a substantial share of high-performance hotels relative to major urban centers; thus, their performance structure is not uniform but strongly polarized. In contrast, the other geographical areas show pronounced clustering, with top hotels concentrated around consolidated leisure destinations, such as Brașov, Sibiu, Constanța, and Prahova. Overall, research using the DEA–TOPSIS method highlights significant spatial disparities that influence both managerial decision-making and regional development policies, affecting the long-term sustainable performance and competitiveness of the Romanian hotel sector. Full article

This paper proposes a two-stage framework integrating Data Envelopment Analysis (DEA) and fuzzy multi-criteria decision-making methods to evaluate the performance of logistics firms in Vietnam. In the first stage, DEA models (CCR, BCC, and SBM) are employed to measure relative efficiency and identify benchmark firms among 15 leading logistics companies. In the second stage, FAHP–FTOPSIS is used to incorporate qualitative and sustainability-oriented criteria and to provide a comprehensive ranking of the efficient firms. The results indicate that a considerable proportion of firms operate below the efficiency frontier, implying substantial opportunities for resource optimization. Environmental and technological dimensions are found to be the most influential factors, while companies implementing green distribution strategies and strong data security practices consistently achieve higher rankings. Sensitivity analysis confirms the robustness and stability of the proposed framework. This study contributes by bridging operational efficiency assessment with broader strategic and sustainability considerations, overcoming the limitations of single-method evaluations used in prior research. The integrated DEA–FAHP–FTOPSIS approach offers managers a practical tool to diagnose weaknesses, prioritize improvement actions, and benchmark against top performers. In addition, it offers policymakers valuable insights to support digital transformation and green logistics initiatives in developing economy contexts. Full article

In the coordinated management of air pollutants and carbon emissions, governments impose differentiated regulatory requirements on gases, while mitigation technologies have heterogeneous abatement potential. However, existing studies on emission quota management, an important mitigation instrument, focus on single gases and neglect integrating multigas policy preferences and heterogeneous abatement potentials, weakening policy responsiveness and scheme feasibility. This study develops a two-stage allocation framework. First, policy preference weights are introduced to evaluate multigas synergistic emission reduction potential and determine maximum quota reduction constraints for each gas. Second, policy preference weights and a non-radial directional distance function (NDDF) are embedded in a zero-sum gains data envelopment analysis (ZSG-DEA) model to capture multigas heterogeneity in policy preferences and reduction constraints, improving applicability and feasibility. Applied to the coal-fired power sector, the results show that, relative to the equal weight scenario, CO₂ incentive intensity rises by 22% under a carbon priority scenario and SO₂ incentive intensity increases by 13% under a pollution priority scenario, while the maximum quota reduction ratios of CO₂ and SO₂ are constrained from 41.75% to 9.18% and from 78.57% to 37.28%, respectively, ensuring alignment with policy preferences and keeping abatement within feasible ranges to support carbon neutrality and pollution control targets, thereby contributing to sustainable development. Full article

Urban metro systems are highly sensitive to seismic disturbances, and the ability of metro stations to manage emergencies effectively has become an increasingly important component of urban resilience. This study develops a resilience-oriented evaluation framework that conceptualizes emergency management as a sequential managerial process encompassing preparedness, response, and recovery. A multi-dimensional indicator system was constructed based on the four resilience capacities—absorptive, maintaining, recovery, and adaptive—and operationalized through a multi-stage Data Envelopment Analysis (DEA) model. The framework enables both overall efficiency assessment and stage-specific diagnosis of managerial weaknesses. Methodologically, the study demonstrates how resilience theory can be operationalized into a network efficiency structure suitable for process-level diagnosis rather than aggregate scoring. A case study of a representative metro station demonstrates the applicability of the proposed method. The results reveal that while preparedness practices are relatively mature, notable inefficiencies exist in real-time response and post-event recovery due primarily to managerial factors such as communication reliability, personnel coordination, and restoration planning. Improvement simulations confirm that targeted enhancements in these management processes can substantially increase overall emergency efficiency. The findings highlight that seismic resilience is not solely determined by physical infrastructure but is heavily dependent on managerial effectiveness across the emergency cycle. The proposed framework contributes a process-oriented, data-driven tool for evaluating and improving emergency management performance and offers practical guidance for metro operators seeking to strengthen resilience under earthquake scenarios. Full article

This study examines how investment efficiency and risk jointly shape sustainable grain supply-chain upgrading. Using firm-level panel data for 25 listed grain supply-chain firms in China from 2015 to 2023, this study examines efficiency–risk structures and their heterogeneity across upstream, midstream, and downstream segments. A three-stage data envelopment analysis (DEA) is applied to measure investment efficiency while controlling for environmental heterogeneity and statistical noise, and a multidimensional investment risk index is constructed using principal component analysis (PCA), with an emphasis on sustainability metrics. The results reveal a clear supply-chain gradient: downstream firms exhibit the highest mean third-stage investment efficiency (crete = 0.633) and scale efficiency (scale = 0.634), midstream firms are intermediate (crete = 0.308; scale = 0.326), and upstream firms remain lowest (crete = 0.129; scale = 0.138). This ordering is also visible year by year, while risk profiles indicate higher exposure upstream and pronounced volatility midstream. Efficiency decomposition shows that upstream inefficiency is mainly driven by scale inefficiency rather than insufficient pure technical efficiency. Overall, efficiency–risk mismatch—manifested as persistent low scale efficiency and elevated risk exposure in upstream, volatility in midstream, and stability in downstream—constitutes a key micro-level barrier to long-term and resilient upgrading. The study thus offers policy-relevant insights for segment-specific interventions that align with sustainable agricultural development: facilitating land consolidation and integrated risk management for upstream scale inefficiency, promoting supply-chain finance and digital integration for midstream risk volatility, and leveraging downstream stability to drive coordinated upgrading and sustainable value creation through market-based incentives. Full article