ABSTRACT Sustainable development transforms production systems, requiring integrated environmental, social, and economic strategies. Energy systems are key agro‐industrial supply chains in developing countries. Brazil's soybean supply chain impacts Gross Domestic Product, mainly through exports. This study evaluates the impact of biodiesel on sustainable efficiency of Brazilian soybean‐producing municipalities, comparing those with and without biodiesel mills. Using a Data Envelopment Analysis approach with a Bayesian second stage, we estimated municipality efficiencies across three dimensions of sustainable development. Results suggest that soybean‐producing municipalities with biodiesel mills are more sustainably efficient than those without. The mean efficiency for municipalities not producing biodiesel was 0.75 on a unit scale; whereas for biodiesel‐producing municipalities, it was 0.81. This is consistent across regional clusters. Findings support that expanding the soybean supply chain by incorporating value‐added activities benefits municipalities by improving socioeconomic conditions. However, complementary policies are needed to strengthen the environmental dimension and ensure an integrated development approach.

Most developing countries are preoccupied with rural development programmes aimed at addressing the key challenges in agriculture, where the majority of their populace is employed and derives its livelihoods. Rural development programmes are basically formulated to address the low agricultural productivity caused by inefficient input use, leading to rural poverty and food insecurity. A study was conducted to measure the technical efficiency and its determinants in 31 District Agricultural Development Plans in five regions of the Southern Agricultural Growth Corridor of Tanzania using a dataset on agricultural production and inputs for the year 2018/19. Data for five input variables (land, labour, farm machinery, fertilizers, and seeds) and two output variables (crops and livestock) were extracted and analyzed through the Data Envelopment Analysis framework to determine DADPs’ efficiency, followed by the Tobit Regression Model to analyze the determinants of the technical efficiency. Based on four intervening factors in agricultural production, namely farm size, agricultural infrastructure, extension services, and farmer field schools, only farm size was statistically insignificant. It was found that extension services would increase technical efficiency by 30 percent, agriculture infrastructure by 22 percent, and Farmer field schools by 12 percent. This implies that there exist technical inefficiencies in many Districts within the SAGCOT. The policy implication of these results is that investment in rural agricultural infrastructure and strengthening the availability and reliability of extension staff could significantly improve DADPs’ efficiency.

Purpose This study aims to analyse productivity change and its determinants in Australian public universities from 2001 to 2022. Design/methodology/approach The study uses bootstrap data envelopment analysis (DEA) and the Malmquist Productivity Index to examine productivity trends. Findings The main findings reveal that technological improvement has been the primary driver of productivity growth in universities. The technological frontier generally drives productivity, while the relative distance of universities from this frontier has changed only marginally, except during the COVID-19 period. During the pandemic, technological advancements slowed, but individual universities improved their relative positions. However, this trend reversed in 2021–2022, marking the worst period for productivity in the last two decades. Practical implications This study highlights the advantages of using multi-inputs and multi-outputs models over traditional ratio measures in accounting literature. The findings are expected to be of interest to universities, higher education policymakers and researchers. Social implications The productivity changes in Australian public universities have significant social implications. Enhanced productivity, driven by technological advancements, can improve educational quality and accessibility, thereby making public higher education more sustainable. This can also lead to better educational outcomes and greater social equity aligned with Sustainable Development Goals. Originality/value This study provides a comprehensive analysis of productivity trends over two decades, contributing to the existing literature on higher education productivity in Australia.

The current research applies a multi-stage approach for measuring and explaining the efficiency performance of 20 general insurers operating in India for the phase 2012–2013 to 2019–2020. In the first stage, the study adopts non-parametric radial data envelopment analysis (DEA) for point and interval estimation of firm-specific efficiency, scale efficiency, returns to scale (RTS) and scale elasticity. The second stage of the study applies panel data regression for regressing technical and scale efficiency scores on the index of market concentration, insurer age, return on shareholders’ capital and the solvency indicator. The outcome of the next stage indicates that the index of market concentration and insurer age are the two contextual variables which are statistically significant, although their impacts on technical and scale efficiency are negative. The influence of the solvency ratio is significant for scale efficiency only. JEL Classifications: C-23, C-61, D-22, G-22

The dynamic evaluation of development performance in nationally recognized first-class courses holds broad implications for improving the quality of online education. This study examines 512 nationally recognized first-class undergraduate online courses on iCourse platform, framing them as an input–output system and drawing on learner-generated online review data. By integrating text mining techniques with data envelopment analysis and the Malmquist index, the study dynamically assesses their development efficiency from 2019 to 2024. The findings indicate that (1) first-class course development maintained relatively high overall efficiency, although sustainability was constrained by limited post-approval investment; (2) course performance displayed a decline–rebound trajectory, primarily driven by teaching innovation; and (3) significant disciplinary differences emerged, with natural science courses exhibiting greater volatility but stronger resilience, whereas humanities and social science courses showed milder fluctuations and experienced late-stage innovation fatigue and resource aging.

Purpose The purpose of this study is to determine the publication outputs based on academic rank, gender and reservation category. It demonstrates whether publication counts differ by gender while controlling for academic rank, reservation category and discipline. Moreover, the study examines the performance of faculty employed through the reservation policy compared with that of faculty recruited through the open category. Design/methodology/approach This quantitative study examines the research productivity of 471 faculty in university settings. Faculty publication records were extracted from Scopus. Bibliometric analysis and statistical methods, including t-tests, chi-squared tests and data envelopment analysis, were conducted in R. Findings The Welch t-test examining research productivity by gender across academic ranks reveals no statistically significant difference in publication counts at the Associate Professor rank. However, at the Assistant Professor and Professor ranks, the p-value indicated a statistically significant difference in the research output. Furthermore, this study provides factual information on the implementation of the reservation policy in an academic setting. The efficiency analysis found no evidence to support the opinion that faculty employed through reservation policies reduce research productivity. In fact, it revealed that faculty recruited under the reservation policy perform on par with their counterparts. In some academic disciplines, they perform as well as, or even better than, those hired under the open category. Originality/value This study emphasises the importance of assessing the impact of the reservation policy to substantiate the stance taken on this initiative. Moreover, to promote transparency in the implementation of this policy and to gain insights into its effects and impact, the study recommends conducting more in-depth research.

ABSTRACT Technical efficiency analysis is widely applied in the cultural and creative sectors. However, little research uses this notion to understand how these economic activities perform at the urban level. In this paper, conceptually, we operationalize urban creativity (UC) as the joint realm of cultural heritage and creative economy; empirically, we measure UC by combining their respective technical‐efficiency scores via entropy‐weighted aggregation. Our research focuses on 107 Italian cities at NUTS‐3 levels and tests the diverse contributions made by the activities mentioned above in the period from 2014 to 2019. Data envelopment analysis and stochastic frontier analysis, both considered in two‐stages approach and output‐oriented, were combined through weighted entropy. The first results show that cultural heritage is the most efficient and decisive contributor to the UC index. In the entropy‑weighted composite, a high CH efficiency score carries greater weight and thus contributes more strongly to overall UC. Conversely, where CH efficiency is weaker, a high CE score can partially compensate, reflecting a synergy mechanism in the composite index. Moreover, we found that small cities tend to make careful and efficient use of resources, while most of the large cities, even when well‐endowed, struggle to govern their CCIs efficiently. This work also examines the contribution of external factors to UC, finding that the influence of GDP, crime, unemployment, educational level, and geographical location (South‐North or West‐East) on UC is limited to specific clusters or peripheral cities. Political faith, understood as local political action, has a generally positive effect and can result in up to +14% improvement, with less equipped cities more likely to benefit from it.

This research assess the efficiency of 15 educational establishments under the jurisdiction of the Puerto Varas municipality using Data Envelopment Analysis (DEA); the aim was to analyze whether the use of resources related to the implementation of educational policies was optimized in 2022. Data from the Ministry of Education and the Quality Agency for Education were used, applying DEA models with constant returns to scale (CRS) and variable returns to scale (VRS), using Excel and R. The results show that six units (DMUs 3, 4, 5, 7, 11, and 14) are fully efficient under both approaches. Nine educational establishments show technical efficiency under VRS but not under CRS, indicating scale problems. Notable are DMUs 1 and 6, which present high technical efficiency but low overall efficiency, suggesting diminishing returns. Particularly, DMU 1 requires proportional and specific improvements, especially in outputs y2, y4, y5 and y6, showing technical inefficiency under VRS. Although it is efficient in the super-efficiency analysis, its performance suggests an oversized scale. Overall, the findings indicate that much of the inefficiencies observed in the analyzed establishments are more related to scale issues than to failures in internal management.

ABSTRACT Against the backdrop of liberal capitalist democracies, the welfare state has emerged as a crucial institution that aims to reconcile market‐driven economic growth with social justice imperatives. The introduction of MGNREGA in 2005 exemplifies welfare intervention with the objectives of poverty alleviation, livelihood security and the empowerment of marginalised communities. Drawing on concepts such as decommodification and the state‐in‐society approach, this study evaluates the efficiency of the MGNREGA across India's diverse sociocultural landscape. By employing a combined approach that integrates data envelopment analysis (DEA) and machine learning (ML) models, such as random forest, this study reveals efficient decision‐making units. This study identifies key efficiency drivers for exploring the relationships among resource allocation, workforce participation and various development outcomes achieved through MGNREGA across all 726 districts of India. By weaving together the threads of regional disparities, state capabilities and implementation outcomes, this study sheds light on the enigma of MGNREGA's uneven success across diverse states in India. Notably, nationally, 36.09% of the districts are efficient, 22.59% are moderately efficient, and 41.32% are inefficient. In essence, while financial factors are necessary for MGNREGA implementation, socio‐economic factors, rural infrastructure development and targeted interventions play pivotal roles in determining efficiency.

This study investigates the impact of market structure on the performance of banks in Pakistan. It explicitly tests two competing hypotheses: the Structure–Conduct–Performance paradigm and the Efficient Structure Hypothesis, providing insights into whether profitability stems from market concentration or efficiency. The study employs the Data Envelopment Analysis approach to measure banking efficiency and uses the concentration ratio to capture market structure. A regression framework is applied, with efficiency and market structure as key explanatory variables. Further, bank-specific controls are included to examine their effects on performance, measured by Return on Assets. Results show that although the concentration of the five largest banks slightly declined, it remains relatively high at 58.5%. Banks, on average, operate at 67% efficiency with an upward trend over time. The findings lend more substantial support to the Efficient Structure Hypothesis, indicating that profitability is primarily driven by technical and scale efficiency rather than market concentration, with individual bank market share affecting performance only as an outcome of efficiency gains. The analysis highlights that efficiency improvements are crucial in enhancing banks’ performance in Pakistan. Over the years, the banking sector of Pakistan has evolved in terms of market structure, efficiency, and banks’ performance. This study interprets the changes in the market structure in the context of the structure conduct performance hypothesis and/or the efficient structure performance hypothesis and answers the question regarding whether market power and/or efficient structure is relevant to the banks’ performance. For policymakers, the results suggest that efforts to improve competitive efficiency, such as encouraging innovation, risk management, and capacity utilization, are more effective than focusing solely on altering market concentration.

The development of Happy Farmhouse tourism in China faces significant challenges of homogenized competition and low operational efficiency. This study aims to evaluate the operational efficiency of these farmhouses and compare the performance across three distinct business entity models: individual farmer, farmer participation, and company cluster. To this end, the study employs a three-stage Data Envelopment Analysis (DEA) model on panel data (2014–2023) from 50 farmhouses in Huzhou, China, to measure technical, scale, and managerial efficiency. Results reveal steady improvements in overall performance but significant efficiency disparities, with company clusters demonstrating the highest efficiency, followed by farmer participation models, and individual farmers demonstrating the lowest. The primary reasons for this disparity are identified as differences in scale efficiency and talent supply. Consequently, our findings provide actionable insights for optimizing rural tourism’s role in sustainable development and rural revitalization, with policy recommendations emphasizing talent recruitment, cluster-based management, and targeted support for different business models.

Efficient allocation of nursing human resources (NHR) is critical for optimizing care quality in integrated medical-nursing elderly care institutions. However, standardized tools for assessing NHR efficiency remain underdeveloped. This study aimed to develop and validate a data envelopment analysis (DEA)-based evaluation system for nursing human resource efficiency in integrated elderly care institutions, with empirical application in clinical settings. Employing a cross-sectional design, the research evaluated NHR efficiency in integrated medical-nursing elderly care institutions. A preliminary indicator system was developed through comprehensive literature review and field investigations, followed by two rounds of Delphi consultations with 17 multidisciplinary experts specializing in nursing management, elderly care administration, integrated medical-nursing operations, health economics, and public health. Expert reliability was determined by assessing response rates, authority levels (qualifications and experience), consensus (agreement rates), and coordination (Kendall's W test). Based on this established indicator system, the DEA model was employed to evaluate the operational efficiency of 12 integrated medical-nursing elderly care institutions in Hainan Province, China. The constructed evaluation system featured a three-level hierarchical structure totaling 68 indicators (9 first-level, 19 second-level, and 40 third-level indicators). Two rounds of expert consultation demonstrated strong participation (response rates: 100% and 94.1%, respectively) and high reliability (authority coefficients: 0.88 and 0.92). Expert consensus was confirmed through statistical analysis (Kendall's W: 0.471 and 0.348; average coefficient of variation: 0.16 and 0.12; all p < 0.001). Subsequent DEA implementation across 12 institutions revealed 5 were fully efficient (OE = TE = SE = 1.000), while the remaining 7 showed varying inefficiency patterns: 4 exhibited pure technical efficiency (TE = 1.000) with scale inefficiency (SE < 1.000), and 3 demonstrated both technical and scale inefficiency (TE < 1.000, SE < 1.000). The nursing human resource efficiency evaluation system developed in this study demonstrated strong validity through a rigorous Delphi expert consultation process, showing high expert engagement and authoritative consensus. The comprehensive three-level indicator system exhibits well-organized structure with strong specialty-specific relevance for integrated medical-nursing care settings. DEA application confirmed the system's effectiveness in objectively evaluating nursing efficiency, supporting its practical utility for healthcare management in elderly care institutions.

The efficiency of large industrial sectors plays a critical role in promoting high‐quality economic growth and enhancing total factor productivity. Data envelopment analysis (DEA) is widely used for such evaluations due to its flexibility and nonparametric structure; however, its core mechanism, which allows each decision‐making unit (DMU) to select optimal weights, often yields extreme and unreasonable weighting schemes that distort efficiency scores and weaken discriminatory power. This study addresses this foundational challenge through three key contributions. First, it introduces the weight extremity function (WEF), a mathematical construct that quantifies the degree of weight concentration within any given weighting scheme. Second, it develops a novel DEA model that incorporates WEF‐based constraints, effectively preventing DMUs from adopting unreasonable weight distributions while preserving the method’s inherent flexibility. The proposed framework transforms the resulting nonlinear programming problem into an equivalent linear formulation, ensuring computational tractability. Third, to eliminate subjective parameter selection, the model employs a volume‐under‐the‐surface calculation method to derive efficiency scores, relying solely on objective statistical properties of the data. An empirical study of China’s large‐scale industrial sectors across 31 provinces (2019–2022) demonstrated the enhanced discrimination capability and evaluation consistency of the proposed approach. The findings reveal significant geographical disparities in industrial efficiency across China, with the proposed model providing more nuanced and robust rankings than existing alternatives.

As domestic violence (DV) poses a critical threat to public health worldwide, this prompts the need for efficient and effective intervention. In Malaysia, although One Stop Crisis Centres (OSCCs) have been offering multisectoral services to DV victims for many decades, an evaluation of the efficiency of these centres has yet to be conducted. This study aimed to assess the efficiency and effectiveness of three Malaysian OSCCs using a two-stage Data Envelopment Analysis (DEA) approach. A total of 153 adult DV victims were recruited from OSCCs in Sarawak General Hospital, Universiti Malaya Medical Centre, and Hospital Universiti Sains Malaysia. The inputs included the number of doctors, nurses, and other personnel whereas the outputs were total response time and service quality, measured via a validated 35-item OSCC-Qual instrument. Stage 1 employed an inputoriented Banker, Charnes, and Cooper (BCC) DEA model to determine how efficiently OSCCs managed resources to minimize response times. Stage 2 used an output-oriented BCC model to evaluate the centre's ability to maximize service quality. The social workers unit recorded notably long mean response times across three centres. Correlation analysis revealed a strong negative association between the number of personnel and the multisectoral coordination dimension of service quality. While most units showed high pure technical efficiency in the input-oriented DEA, scale inefficiencies were shown to be common in all centres. Pure technical efficiency measures how well resources are utilized regardless of scale, whereas scale efficiency assesses whether an organization operates at its optimal size (neither too large nor too small). In the output-oriented model, all centres similarly demonstrated good pure technical efficiency but continued to grapple with scale inefficiencies, especially at Sarawak General Hospital and Hospital Universiti Sains Malaysia. These findings highlight the importance of optimizing operational scale in OSCCs. Tailoring resource allocation and strengthening coordination among multidisciplinary teams could reduce response times and improve care for DV victims.

This study presents a comprehensive comparative analysis of hospital efficiency in Iran using Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodologies. With increasing pressure on healthcare systems to optimize resource utilization while maintaining quality standards, measuring hospital efficiency has become crucial for evidence-based decision-making. The research employs four distinct DEA models, Charnes, Cooper, and Rhodes (CCR), Banker, Charnes, and Cooper (BCC) input-oriented, BCC output-oriented, and Additive models, alongside TOPSIS to evaluate the relative efficiency of Iranian hospitals. By comparing these methodological approaches, this study aims to identify the most suitable framework for hospital performance assessment in the Iranian healthcare context. The analysis incorporates multiple input variables including number of physicians, nursing staff, available beds, and operational costs, against output variables such as patient discharges, outpatient visits, surgical procedures, and bed occupancy rates. The findings provide insights into the consistency and reliability of different efficiency measurement techniques, offering healthcare administrators and policymakers a robust analytical framework for performance evaluation. The comparative approach reveals methodological strengths and limitations in different contexts, contributing to the advancement of healthcare efficiency measurement literature while providing practical implications for hospital management in emerging economies.

This study investigates the role of frontier operational efficiency in predicting financial performance within Egypt’s emerging market. Data Envelopment Analysis (DEA) quantifies operational efficiency, and its predictive power is assessed within a machine learning (ML) framework, extending beyond traditional financial ratios. A Super Learner ensemble is developed, integrating Random Forest (RF) and Categorical Gradient Boosting (CatBoost) with a linear regression meta-learner. The Super Learner enhances accuracy and robustness by dynamically weighting and combining predictions from diverse base models, using a meta-learner to minimize error, reduce overfitting, and improve generalization. Empirical results demonstrate that incorporating DEA significantly improves predictive performance, increasing R² by 3.8% (t = 5.45, p &lt; 0.01). The Super Learner achieves an R² of 0.612, with an RMSE of 0.061 and MAE of 0.046, outperforming both linear regression and state-of-the-art ML models. Feature importance analysis (via CatBoost) identifies net working capital (11.5%) and DEA efficiency (10.0%) as the top predictors. SHapley Additive exPlanations (SHAP) and partial dependence analyses further indicate that DEA efficiency, net working capital, and cash holdings exhibit positive but nonlinear associations with financial performance, while leverage demonstrates a concave, nonlinear relationship. These findings provide practical implications for investors, managers, and policymakers, highlighting the strategic value of operational efficiency. Additionally, the study introduces a scalable, interpretable framework combining frontier efficiency metrics with explainable ML, offering a robust tool for financial decision-making.

Escalating urban traffic problems are impeding city development, underscoring the critical need to better coordinate built environments (BEs) with traffic system states (TSSs). However, the efficiency measurements in the current data envelopment analysis (DEA) model exhibit excessive dependence on input and output data. The determination of weight constraints is also based on subjective judgment. This causes the varying impacts of critical and noncritical indicators on interaction dynamics to be ignored. This study introduces an enhanced DEA model with spatially adaptive weights calibrated by geographically weighted regression (GWR). We propose a TSS indicator that integrates three critical dimensions: traffic efficiency, traffic safety, and travel comfort. In this study, Jinan City is selected as the research area. The coordination assessments refined by incorporating constraints reveal significant disparities: 7.66% are fully coordinated and 63.7% show coordinated conditions, while 28.61% exhibit limited or no coordination. Compared to conventional DEA, the GWR–DEA model demonstrates marginally improved performance, validating the effectiveness of optimized weighting constraints in spatial coordination analysis.

Oil-fired construction machinery (OCM) is a major source of urban air pollutants and CO2 emissions, and electrification is a crucial pathway for improving air quality and achieving China's dual carbon goals; however, its feasibility has not been fully explored. This study uses data envelopment analysis and the analytic hierarchy process to establish a development potential index, covering technical efficiency, economic cost, application scenarios, and charging time and range, with an empirical analysis conducted in Beijing. The findings indicated the high feasibility of replacing OCM with electric alternatives, especially within the low-power range. Based on 2023 registered coding data, it is projected that by 2030, electrification could reduce regional average concentrations of CO, NOx, PM2.5 and VOCs by 12.2 % to 56.4 % and reduce CO2 by 11.7 % to 56.9 %. Owing to economic considerations, small- and medium-sized machinery are particularly feasible for electrification. Key recommendations include prioritizing the electrification of forklifts, lifting platforms, and small-sized machinery in high-emission areas, particularly in central urban districts. Policies such as carbon taxes, carbon markets, and performance grading systems are suggested to incentivize electrification, along with expanding high-emission restriction zones and improving energy infrastructure to support widespread electrification.

As an important part of public service, public sports service governance is an important dimension of the modernization of national governance, and the improvement of its efficiency is related to the health of the whole people and social equity. The existing studies are weak in the application of indicators, lack the analysis of the complex interaction process of different antecedents, and lack empirical support for policy optimization. Method: This study adopts mixed Firstly, data envelopment analysis (DEA) is used to evaluate the dynamic total factor productivity of public sports services. Secondly, fuzzy set qualitative comparative analysis (fsQCA) is used to identify the differentiated driving paths of high/low efficiency caused by multi-factor allocation, such as economy, policy, culture, and social security. Most importantly, based on the R software, the dynamic change of inter-group configuration was analyzed, and the rule of inter-group configuration change was revealed. Result: The 7-year tfpch, effch, and tech index of 31 provinces were 1.125, 1.018, and 1.105, respectively, indicating the improvement of management efficiency. FSQCA results show that there are seven main configurations that can explain the differences in efficiency. Among them, there are 4, 2, and 1 configurations driven by "guarantee factor", "guarantee + information factor", "guarantee + civilization degree", and their coverage is 0.546, 0.348, and 0.092, respectively. The security factors represented by income and environment occupy the most important position in the configuration combination, while the informatization factors and the degree of civilization are second. From the point of view of specific provinces, the higher level of public sports service in the more developed cities in the east is mainly explained by the configuration of "security + civilization degree". The high level of public sports service in Western cities is mainly due to the national guarantee. Based on the above results, this paper draws the following conclusions: First, the guaranteed service provided by the state is the fundamental guarantee of efficient public sports service. Second, the reason for the higher development level of public sports services in more developed provinces may be that knowledge groups with a higher education level and better information use are gathered in cities. Third, for less developed provinces, efficient public sports services are mainly supported by the state, so it is necessary to further explore development in accordance with local conditions.

This relates to the study of the change of productivity of selected banks in the private sector in the twenty-four months of 2014 to 2024 as measured by Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI). Over the last decade, regulated changes, high-speed digitalization and external shock on the external economy have led to tremendous structural changes of the banking sector in India, thereby making the productivity assessment an essential factor in measuring the sustainability of performance. The reason is that the study is concerned with four banks in the private sector with a high level of operation in Kerala and uses a non-parametric and output-based DEA model to identify the change in the total factor productivity and its components. The Malmquist index is broken down into efficiency change and technological change identifying the major contributors to the productivity change with time. The findings indicate that there is a lot of heterogeneity in productivity performance of the sampled banks. Banks that had greater technological penetration and digitization achieved long term productivity increase and others stagnated or contracted as a result of managerial inefficiencies and poor technological advancements. Change in technology was seen as the major factor that led to productivity increase, and efficiency was a relatively minor factor. The results point to the relevance of innovation-based strategies, Quality management practices and adaptive capacity in increasing the productivity in the long term in the banking industry. The study contributes to the existing body of literature by bringing about region-specific and longitudinal evidence regarding banking productivity and presents useful insights to the management of a bank and relevant policymakers concerned with enhancing the resiliency and efficiency of banks in the private sector in Kerala.