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Improving energy efficiency by adjusting the structure of energy consumption types is of great significance for reducing carbon emissions in the short term. The present paper constructs new data envelopment analysis models for evaluating energy utilization under different structural conditions and calculating potential emissions reductions. We conducted empirical research on 30 provinces in China from 2003 to 2019—a time frame that coincides with the instituting of China's “coal‐to‐gas” program. Our results show that technological progress is the main way for China to reduce carbon emissions and that it is possible to reduce the total amount of carbon emissions by 35%. Additionally, optimizing the energy consumption structure following the coal‐to‐gas program guidelines could reduce the country's carbon emissions by a further 25%. Finally, this paper provides specific policy recommendations based on the efficiency analysis results to guide each province in reducing carbon emissions under the conditions of energy demand growth. [ABSTRACT FROM AUTHOR]

This study explores the impact of environmental, social, and governance (ESG) factors on corporations by investigating how board gender diversity moderates the relationship between ESG practices and corporate performance across different regions. Using traditional data envelopment analysis (DEA) and chance‐constrained DEA models, the study analyzes 225 financial service firms over an 11‐year period (2011–2021). Results indicate that high (low) board gender diversity levels significantly influence the connection between the environmental (social) aspects of ESG practices and corporate performance. Thus, the roles of stakeholder engagement and regional distinctions are highlighted in achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive measure of fuel performance, considering economic and environmental factors using Data Envelopment Analysis (DEA). The study uses decentralized production approach, modeling two technology specifications, one with abatement and one without. The study focuses on the Indian public bus transport industry, treating carbon emissions as bad outputs with joint disposability alongside fossil fuels. Additionally, it examines fuel performance drivers through bootstrapped truncated regression. The findings reveal that maintenance practices and vehicle age negatively affect fuel performance, with the latter showing a non‐linear influence. As fuel prices rise, bus company managers increasingly adopt fuel‐saving practices. [ABSTRACT FROM AUTHOR]

This study investigates the impact of activity‐based funding (ABF) on the performance of hospitals by exploiting a natural experiment that happened in the state of Queensland, Australia. To examine the outcome of the reform, the performance of hospitals is measured by the technical efficiency estimated from data envelopment analysis (DEA) models. We try to identify the causal effect of ABF on the technical efficiency of hospitals by incorporating difference‐in‐differences approach in the popular two‐stage DEA framework. We find empirical evidence that ABF improves the technical efficiency of hospitals. [ABSTRACT FROM AUTHOR]

Green development and solutions to environmental problems rely on local conditions and green connections between regions during their development. However, it is less understood whether a region's emission of carbon and pollutants is moderate compared to its development. The study proposes the 'green connection of regional development' concept from the consumption perspective to enhance the understanding and measurement of regional capacity towards green development and responsible production and consumption. Data envelopment analysis was performed to evaluate the local and embodied emissions of eight pollutants for 30 provinces in China from 2012 to 2017. Twenty provinces had optimal capacity of green growth within the region and 15 had an optimal green connection of regional development. Suboptimal levels of overall green growth and green connection of regional development efficiencies were due to insufficient pure technical and scale efficiency levels, with a stronger impact of the former. [ABSTRACT FROM AUTHOR]

Improving green productivity (GP) is critical for achieving global sustainable green development. Yet, past efficiency studies on the African continent have ignored Africa's GP, estimation, and driving factors. Specifically, the role of sovereign debt vulnerability, environmental entrepreneurship, and institutional quality in ensuring Africa's green productivity development is lacking. Therefore, this study built a meta‐frontier biennial weighted modified data envelopment analysis (DEA) model to evaluate green productivity across resource‐intensive and non‐resource‐intensive African countries from 2010 to 2019. Further, the study adopted the global Malmquist‐Luenberger productivity index (GML) to investigate the dynamic changes in African green productivity growth. The results suggest that only a few African countries are efficient, implying more room for improvement for the other countries. The heterogeneity of green productivity across the resource's classification exhibits significant disparity. The technology gap also prevails among the two resource classifications of African countries. The GML index indicates that Africa's green productivity is mainly explained by technical change instead of efficiency change. The bootstrap truncation regression suggests an inverted U‐shaped nexus between growth and green productivity in Africa and resource‐intensive countries (RIC). Green environmental entrepreneurship positively affects green productivity in Africa. On the components of institutional quality, we observed that the rule of law positively influences green productivity in non‐resource‐intensive countries (NRIC) and the whole of Africa. In contrast, political stability, government effectiveness, voice and corruption negatively affected green productivity in NRIC. Also, foreign direct investment (FDI) and sovereign debt vulnerability negatively correlate to green productivity in Africa as a whole and NRIC, respectively. The findings of this study aid in raising awareness of green productivity in developing countries, which is crucial in boosting global green development. [ABSTRACT FROM AUTHOR]

Evaluation of green development is crucial for policy analysis and decision‐making. To address the research gap on green development assessment systems, this study first presents the Super‐efficiency Bounded Adjusted Measure model to gage the green growth index and economic growth index. This analysis involves evaluation of the coupling coordination degree. Through the analysis of the coordination between green growth and economic growth leads to recommendations for each region under consideration in regards to promotion of the components of the sustainable growth. This study presents the model that melds the objectives of the green and economic growth and uses the results for constructing the sustainable development Lorenz curve and Gini coefficient for the analysis of the differences in the sustainable development levels across the regions. The empirical case is presented to show the possibilities for application of the proposed approach. [ABSTRACT FROM AUTHOR]

Regional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios. [ABSTRACT FROM AUTHOR]

Understanding the financial dynamics of healthcare companies is essential for reducing costs while improving access to care and patient outcomes. This study aims to explore the financial efficiency of healthcare companies operating in an emerging stock market. We use conventional and fuzzy data envelopment analysis. We consider liquidity and leverage ratios as input criteria and profitability ratios as output criteria, each taken from Borsa Istanbul for the year 2022. The results indicate that financially inefficient healthcare companies can increase their efficiency by effectively managing debt and working capital, restructuring their asset base, and focusing on equity management. [ABSTRACT FROM AUTHOR]

In this paper, we propose a straightforward way to estimate the Fisher ideal total factor productivity (TFP) index (FI) in cases where price information is unavailable, using 'shadow prices' derived from data envelopment analysis (DEA). A Monte Carlo experiment shows that the shadow price Fisher ideal TFP index (SPFI) can effectively estimate the 'true' FI with relatively small (and stable) errors. The empirical application to the US agriculture sector (1948–2017) further suggests that the SPFI is a (superior) alternative to the traditional Malmquist DEA, especially in dealing with unbalanced panel or time series data when price data are unknown. [ABSTRACT FROM AUTHOR]

Promoting decentralization is currently high on the global agenda. To date, the existing literature in the African context, and more specifically in Cameroon, has not established a link between the size of local authorities and the efficiency of their infrastructure spending. This study therefore attempts to fill this gap in the literature by empirically examining the effect of population size on the efficiency of local public spending in Cameroon. Using the two‐stage Data Envelopment Analysis model on a sample of 100 communes for the period 2017–2020 to estimate composite efficiency scores and the censored Tobit model to determine the effect of population size on the efficiency of local public spending, the results show that population size and density positively and very significantly affect the efficiency of local public spending. In light of these results, we recommend that the state make population size the main allocation key for transfers and subsidies to local authorities and that communes organize themselves into inter‐municipalities to benefit from economies of scale and curb spillover effects. [ABSTRACT FROM AUTHOR]

Previous studies on the efficiency of higher education institutions have generally approached the functions of higher education from a limited perspective, and the studies have followed traditional methods. The aim of this study is to determine the teaching, research, and third mission technical efficiency of universities, to rank efficient universities, to identify potential areas for improvement, and to examine the determinants of efficiency scores and the differences in scores in terms of various variables. In this study, the efficiency of 187 universities was measured by data envelopment analysis. Efficiency criteria were weighted by both objective and subjective multi‐criteria decision‐making (MCDM) methods and ranked by different MCDM methods. Beta and Linear regression were performed to determine the factors on efficiency scores. According to the results of the study, the most efficient universities are in the fields of research, teaching, and third mission, respectively. Budget criterion is the most important input criterion. Number of graduates, number of publications, and number of social responsibility projects are the most important output criteria. Having an older history positively predicts teaching and research efficiency. The size of universities and being located in developed regions negatively predict research and third mission efficiency. There is a positive relationship between rankings in teaching, research, and third mission. This research provides more comprehensive information to universities by considering teaching, research, and third mission efficiency separately. Second, this research proposes a new methodology for higher education efficiency measurement by integrating DEA and hybrid MCDM. Finally, this research provides new insights by analyzing factors related to teaching, research, and tertiary mission efficiency. [ABSTRACT FROM AUTHOR]

In developing nations such as India, organic farming has emerged as an alternative and sustainable agricultural strategy. Therefore, it is critical to determine the key determinants that could effectively enhance the efficiency of organic farming in India and promote its expansion. This study focuses on the macroeconomic determinants that affect the efficiency of organic farming in India. The study utilizes data from 2012-13 to 2017-18 for 22 Indian states and empirically analyses the macroeconomic determinants of organic farming efficiency by incorporating the double bootstrap Data Envelopment Analysis (DEA) method. The efficiency of organic farming is assessed by considering three crucial determinants: human capital, agricultural infrastructure, and agricultural public spending. The empirical evidence suggests minor impact of agricultural infrastructure and public spendings have overall significant and positive impact on organic farming efficiency. Moreover, the allocation of various funds toward agricultural public expenditure would likely to enhance the adoption of organic farming practices in India. [ABSTRACT FROM AUTHOR]

Distribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility. Plain Language Summary: The past decades have witnessed an unbridled growth in goodness‐of‐fit metrics of hydrologic models. These metrics may satisfy the needs of hydrologists but lack conforming theory and principles. This state of affairs (a) elicits improper model training and evaluation, (b) provokes and supports misguided inferences, (c) impedes statistically‐principled uncertainty quantification, metric standardization and development of universal model benchmarks and (d) obfuscates determination of whether the model has finished learning. What is more, most hydrologic model evaluation metrics in use today are rather exclusive in how they quantify model performance using only single‐valued simulated output of the quantities of interest. Predictive distributions derived from (quasi)‐Bayesian methods or ensembles are usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures (scoring functions) such as the mean absolute error. This implies a large loss of information. This paper develops a distribution‐based approach to hydrologic model evaluation and diagnostics. Distribution evaluation opens the necessary theory and guidelines for development of robust information‐theoretically principled metrics of watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. The hydrograph functionals offer a statistically principled means for hydrologic model evaluation, diagnostics and selection. Key Points: Scoring rules of hydrograph functionals provide an information‐theoretically principled means for watershed model training, evaluation, and diagnosticsWe present strictly proper (divergence) scores for flood frequency analysis, flow duration, and recession curvesPropriety and elicitability offer useful working paradigms for metric development of hydrograph functionals [ABSTRACT FROM AUTHOR]

This study analyses the impact of corporate social performance (CSP), proxied by the Environmental, Social and Governance (ESG) Index and its pillars and dimensions, on the environmentally adjusted efficiency (EAE) of 239 listed companies, over the period 2016–2021, in the global energy sector. The EAE index, estimated via data envelopment analysis (DEA), assesses the efficiency of companies by considering economic aspects alongside CO2 and energy consumption reduction targets. Our results support the hypothesis that only above a certain level of performance, do CSP practices positively influence EAE, so there is a non‐linear U‐shaped association between the overall ESG score and EAE. The same holds for the social and governance pillars, and the human rights and management subspecific dimensions, on EAE. Other ESG attributes show a positive linear relationship with EAE, namely the environmental pillar, the environmental innovation dimension and the shareholder dimension. Our paper has significant implications for companies, policy makers and researchers insofar as it estimates EAE and indicates which CSP practices should be invested in over the long term to have a positive effect on it. [ABSTRACT FROM AUTHOR]

Copyright of World Food Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article describes the construction of the Luenberger soil quality indicator (SQI) using data on crop yield, non‐soil inputs, and soil profile from three irrigated agroecological zones of Punjab, Pakistan, namely, rice–wheat, maize–wheat–mix, and cotton–mix zones. Plot level data are used to construct a soil quality indicator by estimating directional distance functions within a data envelopment analysis (DEA) framework. We find that the SQI and crop yield relationships exhibit diminishing returns to improving soil quality levels. Using the constructed SQI values, we estimate linear regression models to generate weights that could be used directly to aggregate individual soil attributes into soil quality indicators without the necessity of fitting a frontier to the crop production data. For wheat and rice production, we find that SQI is most sensitive to changes in soil electrical conductivity (EC) and potassium (K). The SQI has direct relevance for site‐specific decision‐making problems where policymakers need to price land resources and conservation services to achieve agricultural and environmental goals. [ABSTRACT FROM AUTHOR]

Wastewater management decision‐making is complicated because of: (1) a complex regulatory structure, (2) the wide variety of conflicting expectations by stakeholders external and internal to the responsible utility, and (2) constrains including regulatory requirements, available technologies and practices, and customer willingness to pay. This review synthesizes the results from over 200 papers published since 2000 and presents a decision‐making structure and process which is (1) science and fact‐based, (2) reflects sustainability, (3) clear and transparent, (4) inclusive, (5) produces an objective‐oriented decision, (6) scalable, (7) repeatable, and (8) efficient. Tools supporting the decision‐making process are reviewed, including Multi‐Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA). Ultimately it was determined that engagement of decision‐makers and relevant stakeholders to assess their values and preferences, coupled with supporting data and analyses, is necessary to reach a decision that, critically, has the support needed for it to be implemented. The results demonstrate that an understanding of the components of the decision process, coupled with an orderly process, enables good wastewater management decision‐making. Practitioner Points: A decision‐making structure and process leading to the selection of implementable solutions is presented.The process possesses the following attributes: (1) science and fact‐based, (2) reflect sustainability, (3) clear and transparent, (4) inclusive, (5) produce an objective‐oriented decision, (6) scalable, (7) repeatable, and (8) efficientAn extensive summary and analysis of tools supporting the decision process are provided, including Multi‐Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA).The critical role of internal and external stakeholders and differentiating their involvement relative to decision‐makers is emphasized. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nurturing specialized and sophisticated enterprises is a vital strategic initiative to mitigate the dependency on crucial technologies and to promote the competitiveness of Chinese manufacturing industry. Basing on a technology, organization, and environment (TOE) framework, combined with geographic concentration, qualitative comparative analysis (QCA), and data envelopment analysis (DEA) methods, this article analyzes the conducive business environment for the growth of specialized and sophisticated "little giant" enterprises (LGEs) from configurational perspective. It takes the quantity and density of LGEs in different regions of China as the consequence event. The findings show that: (1) the spatial distribution of LGEs is highly uneven with a geographic concentration index (CR5) of 0.436 and a Herfindahl–Hirschman Index of 0.0599. (2) The presence of numerous technology‐oriented SMEs and a "favorable" business environment are not necessarily the regions where a larger number of LGEs are fostered. Indeed, the harmonious interplay of TOE is more important for achieving optimal outcomes. The differential configurations contributing to the regional output variations of LGEs can be classified with three patterns: technology‐organization‐environment synergistic promotion type (H1), innovation stimulation through effective financial support type (H2a), and compensatory type for inadequate innovation resources (H2b). While these configurations may lead to similar outcomes, they do not yield the same effectiveness. (3) The DEA reveals a tendency for "over‐optimization" in improving regional business environment under the TOE framework, indicating the presence of "input redundancy" and corresponding "output loss" issues in TOE factors. Local governments must adhere to the "barrel principle" and optimize the business environment that aligns with development of LGEs through the concept of dynamic adaptation. Adopting an organizational mindset of "stringing beads into chains" can help construct regional industrial chains with distinctive features. [ABSTRACT FROM AUTHOR]

In this article, we estimate the technical efficiency ratio for a sample of 24 financial institutions in Ecuador over the period 2015–2019. To do so, we first apply a non‐parametric method such as the data envelopment analysis (DEA). Under variable returns to scale and output‐oriented "Interest Income", the average technical efficiency for the 24 banks is 84.26% and, under output‐oriented "Other Operating Income", the average technical efficiency is 73.22%. We find that the group of large banks has higher levels of efficiency and that there are still opportunities for improvement in the group of medium and small banks. We also estimate that 80% of Ecuadorian banks need to make potential improvements either by increasing their output or reducing their inputs, such as salary costs, to increase the efficiency. In addition, the growth of the banking sector is crucial to achieve the financial development in Ecuador, being significant the efficiency indices, which show that a 1% increase in banking efficiency could increase its size by USD 605 million. By using the random effects panel data econometric model, we demonstrate that the growth of the banking industry is mostly influenced by the concentration of deposits and taxes. [ABSTRACT FROM AUTHOR]

For meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two‐stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi‐activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre‐process and industry have a significant and positive impact on energy efficiency in China. [ABSTRACT FROM AUTHOR]

The Luenberger-Hicks-Moorsteen (LHM) total factor productivity (TFP) indicator has sound theoretical properties, but its decomposition yields indeterminate components of technical change and scale efficiency change that can become infeasible. The current paper decomposes the approximating Bennet indicator, which results in determinate components of technical change, technical efficiency change, scale efficiency change and mix efficiency change that are always feasible. The application focuses on the German dairy-processing sector, an important postfarm supply chain actor. We compute 558 growth rates for the period 2011-2020. The results show that the LHMapproximating Bennet indicator decreases by on average 1.14% p.a., with substantial annual fluctuations. The underlying components of output- and input-oriented technical change also fluctuate substantially, and often conflict. Moreover, output- and input-oriented TFP efficiency change fluctuate moderately on average, which is mainly driven by scale efficiency change and mix efficiency change. The components of technical efficiency change remain relatively stable on average. Indeterminateness is a relevant problem when decomposing the original LHM indicator for the current sample: depending on the specification, the proportion of infeasibilities when decomposing the original LHM indicator ranges between 6.09% and 15.95%. Our proposed determinate decomposition is thus a valuable complement. [EconLit Citations: D24, D25, Q13]. [ABSTRACT FROM AUTHOR]

In data envelopment analysis, cross‐efficiency evaluation stands out as a valuable tool for ranking the effectiveness of decision‐making units (DMUs). However, existing research commonly assume that DMUs are randomly classified as either collaborators or opponents of the evaluated DMUs. Unfortunately, few studies have considered the presence of conflict behaviour and beneficial relationships among DMUs during cross‐efficiency evaluation. To address this research gap, this study proposes an innovative approach. Firstly, the proposed framework incorporates an interval cross‐efficiency environment to accommodate the inherent uncertainty and fuzziness in the efficiency scores of DMUs. Secondly, two definitions, namely task conflict cross‐efficiency and relationship conflict cross‐efficiency, are introduced by combining the characteristics of the interval cross‐efficiency matrix with conflict behaviour. To measure the strength of the conflict between DMUs, a novel measurement method is proposed, forming the foundation for constructing two cross‐efficiency secondary programming models based on conflict behaviour. Thirdly, the Spearman's rank correlation coefficient is employed to measure the strength of beneficial relationships among DMUs. Subsequently, two cross‐efficiency secondary programming models are developed based on beneficial relationships. To obtain the final scores, the efficiency scores of the four models are integrated using the Shannon entropy method. Finally, two illustrative applications are presented to demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Applying a spatialized data envelopment analysis, this paper estimates and analyzes the efficiency of European Union NUTS‐2 regions during the period 2000–2014. The space‐dependent efficiency scores estimated with the proposed model show a bimodal distribution that is not detected by the aspatial approach. The results confirm the crucial role of location of production units, offering important new insights on both the causes of regional disparities in labor productivity and the observed polarization of the European distribution of per capita income. The findings further suggest significant differences for the two subgroups (spatial clusters) of regional economies found in the correlation analysis between the efficiency scores and the components of the EU Regional Competitiveness Index. Consequently, policies suitable for one group of regions may not be suitable or appropriate for the other. [ABSTRACT FROM AUTHOR]

The labor movement from rural areas and the remittance flow from migrants is a common household livelihood strategy in rural Bangladesh. While migration can offer economic benefits through remittances, it can be a source of hardship for migrants and their families due to societal culture. This study examines the differences in farm productivity and technical efficiency between female and male labor migrants by focusing on female and male laborers who have lived away from their homes for 6 months or more within the country and its reflection on farm production. Using data on 2271 rice plots from Bangladesh Integrated Households Survey in 2018, we estimate plot‐level stochastic meta‐frontier approach for households with female‐labor migrants and male‐labor migrants separately emphasizing technological difference and heteroskedastic technical efficiency. The empirical result shows that the female‐labor migrants' farms have 10.3% lower production frontier (maximum frontier yield) and 6.1% higher technical efficiency than male migrants' farms, indicating that they have 4.2% lower productivity. Lower production frontier reflects lower management ability and less attention to farm practice. Moreover, the study reveals that female‐labor migrants' farms are closer to the meta‐frontier, suggesting smaller technology gaps. However, some farmers failed to achieve the highest possible output in relation to the meta‐frontier, indicating that farmers can boost their production by adopting and disseminating new rice production technology. [ABSTRACT FROM AUTHOR]

Fault detection based on canonical correlation analysis (CCA) has received increased attention due to its efficiency in exploring the relationship between input and output. However, traditional CCA may generate redundant features in both the input and output projections while maximizing the correlations. In this paper, sparse dynamic canonical correlation analysis (SDCCA) is developed for dealing with the fault detection of dynamic processes. Through posing sparsity in the extraction of features, the interpretability of canonical variates is enhanced attributed to the sparsity of canonical weights. Based on the SDCCA model, the T2 monitoring metric is established for fault detection. Moreover, the upper control limit (UCL) based on T2 monitoring metrics is determined by the kernel density estimation (KDE) method to avoid the violation of the Gaussian assumption. The superiority of the proposed SDCCA‐based fault detection method is illustrated through a comparative study of the Tennessee Eastman process benchmark. [ABSTRACT FROM AUTHOR]

Centralized resource allocation and target setting (CRATS) refers to a process in which top management has the power and responsibility to allocate limited input resources and to arrange expected output targets among its own decision‐making units (DMUs). Data envelopment analysis (DEA) has been widely used to investigate the CRATS issues by considering the production process of DMUs as a single‐stage structure, namely, a black box. This study proposes a novel centralized two‐stage network DEA because the DMUs with a two‐stage production process are a common phenomenon in real‐world cases. The proposed model is applied to determine the optimal CRATS plan for an empirical case, where 27 retailers belong to an automobile parts supplier in Taiwan. [ABSTRACT FROM AUTHOR]

The need of translating increasing outlays into better outcomes on various public programmes has attracted the attention of researchers worldwide to focus more on the quality of public spending, often assessed in terms of its efficiency. The present article is a contribution to the existing literature on the subject in that it assesses the efficiency of government expenditure of Indian states for two most basic social services—elementary education and nutrition during 2014–2015 and 2018–2019 using data envelopment analysis and Malmquist productivity index techniques, in both input‐oriented as well as output‐oriented settings under variable returns to scale assumption; it constructs the average performance index for children at elementary education and malnutrition index for women and children to utilise them as output indicators; and it adopts a robust bootstrap truncated regression procedure to avoid various econometric issues while estimating the impact of selected environmental factors on bias‐corrected inefficiency scores. The major findings of this article seem to suggest that, while states should be provided greater flexibility in their fiscal operations, they have to complement their fiscal autonomy with good governance to improve the efficiency of their spending on basic social services. Towards this end, states like Bihar, Jharkhand and those belonging to North‐Eastern and Himalayan regions need special attention with a different set of development policies. [ABSTRACT FROM AUTHOR]

Accessibility to financial resources is considered a prevalent problem in the agricultural sector. We develop an approach to quantify the long‐term opportunity costs of financial constraints in relation to peers who do not face any financial constraints. Using data on past financial performance, we assess creditworthiness and the size of an additional accessible bank loan to farmers. Combining this with data on reported expenditure, we determine the accessible finance. We quantify the opportunity cost as the forgone dynamic profit (intertemporal profit in current‐value terms) from financial constraints. Using data envelopment analysis, we apply our approach to 264 specialised Dutch dairy farms for the years 2006–2017 and explore the potential impact of changes in finance provision for several scenarios. Our results show an increasing gap between frontrunners and other farmers, as the latter generate progressively less dynamic profit in comparison to their best peers. The gap between the dynamic profit of the average farm and that of its best peers from their production and investment decisions made over the span of 1 year grew from €40,040 in 2009 to €114,548 in 2017. However, the growth is not driven by insufficient access to finance. Financial constraints can only explain 6% of the forgone dynamic profit in 2009 and as little as 1% for 2017. The number of farms classified as financially constrained in comparison to their peers decreases in our sample from 44% in 2009 to 8% in 2017. This suggests that non‐financial factors are driving the growing gap. [ABSTRACT FROM AUTHOR]

We study the effect of Law 81/1993, which introduced a different rule for the election of mayors, on the technical efficiency of Italian cities over the period 1998–2006. Since 1993, municipalities below 15,000 inhabitants vote with a single‐ballot system, whereas cities above 15,000 inhabitants are subject to a double ballot. We first estimate the output‐oriented technical efficiency of municipalities through data envelopment analysis, and then we perform a regression discontinuity design analysis by exploiting the exogenous change in the electoral scheme at the 15,000 inhabitants cut‐off. We find evidence that municipalities voting under a double‐ballot rule show lower levels of efficiency—in terms of provision of public goods—compared with cities voting under a single‐ballot scheme, by about 6.5%. This effect is driven by inefficiencies registered in specific sectors, that is, police, education, and maintenance of roads. Furthermore, the overall lower efficiency of municipalities voting under runoff is related to the different level of educational attainment of elected mayors, whereas it is not driven by the features of Law 81/1993 relating to the number of lists forming a coalition in support of mayoral candidates and to disjoint votes. [ABSTRACT FROM AUTHOR]

This document is a collection of abstracts from the journal "Reviews in Aquaculture." The abstracts cover a range of topics related to aquaculture, including the use of microalgae in aquaculture feed, the role of gut microbiota in lipid metabolism in aquatic animals, drying processes for seafood products, the impact of organic waste from marine fish farms, and the application of electrochemical water treatment in recirculating aquaculture systems. The abstracts provide a brief overview of each topic, highlighting key findings and areas for future research. The article on the parasitic hydroid and bivalve mollusks explores the impact of the hydroid on its host and the need for further research on the physiological and meat quality changes in parasitized bivalves. The article on fishmeal as a protein source in aquaculture feed discusses the challenges and potential solutions, such as functional feed additives, to improve protein utilization and enhance fish growth and health. The article on medicinal plants and their active compounds in the treatment of viral diseases in aquaculture reviews recent studies on their efficacy and highlights the potential for preventing excessive proliferation of the hydroid and its interaction with climate change. The article on efficiency scores in aquaculture emphasizes the importance of selecting appropriate variables and sample sizes for accurate estimation and suggests using data envelopment analysis and stochastic frontier analysis for robustness testing. The article on diatoms in sustainable aquaculture explores their potential as a source of nutritious feed and their [Extracted from the article]

As the world population grows, the aquaculture sector faces challenges in the use of production systems to ensure the adequacy of the food supply. Technical efficiency analysis has been shown to be useful for improving resource management and production. This study provides a literature update and uses meta‐regression analysis to examine the variations in aquaculture mean technical efficiency (MTE) scores based on 101 studies published from 1998 to 2023. The findings show that studies with larger model dimensions, different output measures, an input‐oriented model specification and that analyse the exogenous variables of efficiency scores, freshwater aquaculture, and European data produce a narrower variance in MTE scores. In contrast, studies that use panel data and nonparametric data envelopment analysis (DEA) generate wider variance in MTE scores. As a result of these investigations, the findings suggest the importance of selecting appropriate variables and sample sizes for efficiency estimates. Robustness checks of efficiency scores from the DEA and stochastic frontier analysis (SFA) approaches are recommended for future studies. Different policies and regulations should be implemented in different countries and across different farming systems. [ABSTRACT FROM AUTHOR]

Renewable energy sources have been utilized by countries worldwide, but offshore energy exploitation projects remain highly promising for both the world and New Zealand specifically. The selection of equipment installation locations can be challenging and requires significant investment, prompting the need for optimization in offshore energy exploitation. Wind and wave location (WWL) integrated sites offer a highly potential solution for reducing installation costs and enhancing efficiency in renewable energy exploitation. However, the decision‐making process for selecting equipment locations is crucial and directly affects the outcome. This study aims to evaluate and consider sustainable and effective equipment installation sites based on both quantitative indicators and behavioral psychology of decision making. To achieve this goal, the study utilizes the prospect theory Data Envelopment Analysis (PT‐DEA) model, which builds on PT to analyze and evaluate the effectiveness of offshore installation site selection with consideration for human psychology. By combining the results of quantitative indicators and decision‐making behavior, the study identifies sustainable and effective offshore installation sites, recommending appropriate equipment installation locations WWL. The study is also compared and referenced with relevant plans, projects, and development policies in New Zealand for confirmation. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis (GSA) enhances our understanding of computational models and simplifies model parameter estimation. VarIance‐based Sensitivity analysis using COpUlaS (VISCOUS) is a variance‐based GSA framework. The advantage of VISCOUS is that it can use existing model input‐output data (e.g., water model parameters‐responses) to estimate the first‐ and total‐order Sobol' sensitivity indices. This study improves VISCOUS by refining its handling of marginal densities of the Gaussian mixture copula model (GMCM). We then evaluate VISCOUS using three types of generic functions relevant to water system models. We observe that its performance depends on function dimension, input‐output data size, and non‐identifiability. Function dimension refers to the number of uncertain input factors analyzed in GSA, and non‐identifiability refers to the inability to estimate GMCM parameters. VISCOUS proves powerful in estimating first‐order sensitivity with a small amount of input‐output data (e.g., 200 in this study), regardless of function dimension. It always ranks input factors correctly in both first‐ and total‐order terms. For estimating total‐order sensitivity, it is recommended to use VISCOUS when the function dimension is not very high (e.g., less than 20) due to the challenge of producing sufficient input‐output data for accurate GMCM inferences (e.g., more than 10,000 data). In cases where all input factors are equally important (a rarity in practice), VISCOUS faces non‐identifiability issues that impact its performance. We provide a didactic example and an open‐source Python code, pyVISCOUS, for broader user adoption. Plain Language Summary: Global sensitivity analysis is a method used to better understand and estimate parameters in computational models. VarIance‐based Sensitivity analysis using COpUlaS (VISCOUS) is a framework for this purpose. It estimates the sensitivity of model outcomes to different uncertain model input factors by using the existing input and output data (e.g., water model parameters and responses). This study improved VISCOUS and tested it with various functions. We found that its performance depends on the number of input factors, the amount of input and output data available, and our ability to determine VISCOUS's parameters. VISCOUS is good at estimating the importance of individual input factors, even with limited data (e.g., 200) and numerous input factors. It always correctly ranks input factor importance, whether individually or collectively. When estimating the importance of input factors together, VISCOUS is recommended when the number of input factors is not very high (e.g., <20), as it is challenging to generate enough input and output data for estimating VISCOUS's parameters. When all input factors hold equal importance (though rare in practice), VISCOUS's performance is impacted due to the difficulty of estimating VISCOUS's parameters. To help people use VISCOUS, we provide an example and an open‐source Python code, pyVISCOUS. Key Points: We improve the VarIance‐based Sensitivity analysis using COpUlaS (VISCOUS) global sensitivity analysis framework in its handling of marginal densities of the Gaussian mixture copula modelWe evaluate VISCOUS and demonstrate how its performance is affected by function dimension, input‐output size, and non‐identifiabilityWe provide a didactic example and an open‐source Python code called pyVISCOUS to make VISCOUS easier to understand and apply [ABSTRACT FROM AUTHOR]

State road transport undertakings (STUs) in India are public utility services that link diverse terrains and remote and hilly areas throughout the country and play a vital role in enhancing public mobility. These organizations are motivated to work through a sense of social responsibility. For example, they operate in low‐revenue‐generating regions to serve the public. Therefore, they require regular performance monitoring and constant efforts toward course corrections. Existing literature on the performance evaluation of STUs ignores the internal structures of the passenger transportation process and treats the process as a "black‐box." Consequently, it fails to identify the precise cause of inefficiencies, making course corrections in a multistage transportation process difficult. To close this research gap and comprehensively evaluate the performance of STUs, considering their internal structure and based on long‐term optimization, this study employs a slacks‐based dynamic network data envelopment analysis (DNDEA) approach to deconstruct India's publicly owned passenger road transportation process into operations and revenue divisions, subjected to a multiperiod performance evaluation from 2014–2015 to 2017–2018. Moreover, we utilize panel data regression to explore other influential factors not included in the DNDEA, explaining the efficiency variation across STUs. This study is the first to explore the internal structure of STUs in India and perform a comprehensive evaluation of STUs, considering their internal divisions and multiple‐period long‐term optimization. The DNDEA results highlight evidence of capital misallocation in India's public road transport sector. While operational inefficiency can be attributed to improper utilization of resources, price regulation is the major source of inefficiency in the revenue division. Fixed effects panel data regression revealed that STUs with higher bus utilization and passenger lead are more efficient. The contribution of this study lies in its innovative approach of using DNDEA to analyze India's passenger road transportation sector and its inner constructs, providing insights into the efficiency scores of STUs and their dynamic changes in one model. Managers and policymakers can use the findings of this study to revise and frame policies targeted at improving STU efficiency. [ABSTRACT FROM AUTHOR]

We engage parametric and non‐parametric approaches to analyze unbalanced data (2012–16) from 225 credit unions in Ghana. The non‐parametric analysis involves using data envelopment analysis to assess the technical efficiency of the credit unions. We show that most of them are not technically efficient. The average five‐year overall technical efficiency and pure technical efficiency scores of a credit union are 0.87 and 0.91 respectively. Targeted at inquiring whether manager bonding significantly connects with the technical efficiency of credit unions, the parametric analysis which involves the use of probit and logit regression techniques, shows that, generally, the bonding of managers hurts the technical efficiency of credit unions. We are, thus, led to the conclusion that bonding of managers may not be in the best interest of credit unions in Ghana. [ABSTRACT FROM AUTHOR]

This study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers. [ABSTRACT FROM AUTHOR]

This work investigates the Italian judicial system and discusses the designing of a policy reform to boost the efficiency of courts, considering the human resources available as well as the expenditure generated by the process to deliver justice. The authors present the benchmarks and shed light on how policy makers embarking on such a process of reform may be misled by inappropriate model definition. In detail, the authors propose a comparative analysis of technical efficiency (TE) scores computed using data envelopment analysis, adopting the same output (number of settled cases) and two alternative sets of inputs (judicial expenditure and human resources). According to the results, without considering the information extracted from the two complementary benchmark analyses, policy makers might be misled in the reform process. Regarding the elements that affect the performance of courts, it may be possible to improve the efficiency of judicial districts by working on judicial procedures. Hence, these are the domains where interventions by policy makers may prove successful. As for policy implications, the models and benchmarks presented here could be used to devise a reform of the judicial system aimed at enhancing its TE, while also keeping public expenditure under control. [ABSTRACT FROM AUTHOR]

This article looks beyond Z‐score and proposes a novel methodological framework to build an all‐encompassing indicator of bank stability for individual banks using the optimisation‐based 'benefit‐of‐the‐doubt (BoD)' approach. Unlike other available approaches, this approach is totally data‐driven and generates endogenous weights to aggregate sub‐indicators of bank stability and dimensions. Further, the final outcomes are not limited to a scalar measure of bank stability. The unique optimal weights offer valuable policy‐relevant insights and highlight the most precarious areas of stability, which demand the immediate attention of the bank's management and the industry regulators for both micro‐and macro‐level policymaking. Using the data of Indian public sector banks operating in the year 2018, the study illustrates the proposed framework to obtain a holistic indicator of bank stability, defined on 14 ratio sub‐indicators and 5 broad dimensions (soundness, asset quality, profitability, management efficiency, and liquidity) of bank stability. [ABSTRACT FROM AUTHOR]

The population of the world is increasing, and product demand is increasing, making a linear economy ineffective, and therefore, this situation makes the circular economy (CE) a requirement. Economic, social, and environmental factors all play a role in the CE. Also, logistics activities are critical parts of the CE. Hence, this study's goal is to create a framework for evaluating organizations' CE logistics performance. The main contribution of the study is the creation of a framework to assess the circularity performance, including various logistics activities, and measure the performance of logistics activities. To establish a list of criteria, 30 experts were interviewed. The criteria were ranked using fuzzy statements. The selected multi‐criteria decision‐making (MCDM) method, data envelopment analysis, is applied using IBM ILOG CPLEX Optimization Studio version 20.1.0. [ABSTRACT FROM AUTHOR]

Circular agriculture is vital to achieve a substantial reduction of greenhouse gas (GHG) emissions. Optimizing resources and land use are an essential circularity principle. The objective of this article is to assess the extent to which land optimization can simultaneously reduce GHG emissions and increase production on dairy farms. In addition, we explore the potential reduction of GHG emissions under four different pathways. The empirical application combines the network Data Envelopment Analysis (DEA) with the by‐production approach. This study focuses on a representative sample of Dutch dairy farms over the period of 2010–2019. Our results suggest that farms can simultaneously increase production and reduce GHG emissions by both 5.1%. However, only 0.6% can be attributed to land optimization. The land optimization results show that on average 25.3% of total farm size should be allocated to cropland, which is 6.7% more than the actual land allocation. GHG emissions could be reduced by 11.79% without changing the level of inputs and outputs. This can be achieved by catching up with the mitigation practices of the best performing peers. [ABSTRACT FROM AUTHOR]

One of the most significant problems in the development of quantitative models to assess the performance of decision‐making units (DMUs) is the availability of information coded into variables and indicators that are proxies for the relevant dimensions of the models: resources or inputs, products or services or outputs, and heterogeneous contextual factors including environmental factors. Data envelopment analysis (DEA) and balanced scorecard (BSC) are two of the best‐known and applied tools to model and measure the performance of DMUs. Within the information set requested to model the performance, the most critical and sensitive variables and indicators related to the intangible capital of organizations, which includes with a primary role the intellectual capital (IC). Although they are very different, DEA (based on linear programming) and BSC (including a set of indicators along four dimensions) have recently been combined to try to address the problem indicated earlier, namely, having a set of variables and indicators available to better measure the performance of DMUs. We apply a three‐level methodology combining (i) a series of systematic reviews, (ii) a bibliometric analysis of all the published works found, and (iii) an analysis of the so‐called grey literature contained in the reports of knowledge‐based organizations. The main results obtained are (1) a comprehensive survey and mapping of all scientific works combining DEA, BSC, and IC including the gender dimension; (2) an integral and inclusive list integrating all indicators found in both published works and reports, reclassified according to the main dimensions of the IC. [ABSTRACT FROM AUTHOR]

This paper presents a literature review on network models in data envelopment analysis (DEA) focused on the slack‐based network model called slacks‐based measure‐network data envelopment analysis (SBM‐NDEA). The search was carried out in an international database, resulting in 97 publications in the period from 2009 to 2021. From these publications, a descriptive analysis is performed, the evolution of the literature is mapped, the articles are organized into categories, and the main methodological references for the models and applications used are identified. Also, the most used models and their main characteristics are described, in addition to the analysis of the applications. Moreover, the sectors and countries with the highest number of applications are also identified. Through this review, it was possible to observe an increase in the number of network studies using the SBM‐NDEA in recent years. Finally, directions for future research in SBM‐NDEA are suggested. [ABSTRACT FROM AUTHOR]

When evaluating the performance of Research and Development (R&D) and Scientific and Technological (S&T) transformation activities in universities, the existing studies do not contain prior strategy analysis and inequality analysis. In this regard, prior strategy reflects the relative attention paid by universities to R&D and S&T transformation activities, while inequality analysis assists us in better understanding technological inequality in efficiency across universities. Additionally, this study contributes to the existing literatures by proposing new concepts of window‐based cross‐efficiency, window‐based relative weights and a network Data Envelopment Analysis model. Our framework is able to evaluate the efficiency, perform priority analysis and identify the sources of technological inequality. Empirically, this study focuses on universities in Chinese provinces from 2007 to 2019. This study is of high policy practice, since it provides policy makers with how each university has performed during the examined period and what are the underling factors driving efficiency changes. [ABSTRACT FROM AUTHOR]

We explore the role of staff training and experience for banks' efficiency and profitability, while accounting for size, solvency, liquidity and business model of the banking sector. We try answering two questions: (i) are banks with higher levels of staff training and experience more efficient and profitable and (ii) do banks that spend more to increase the quality of their staff have better outcomes in terms of NPLs, in case of a downturn? We use two novel sources of microdata from a one‐off survey about banks' HR policies and a database on banks' balance sheets and P&Ls regarding the Romanian banking sector. We apply the Data Envelopment Analysis (DEA) approach to estimate production frontiers and rank banks by cost and income efficiency. Our main findings support the conclusion that it pays off to invest more in the quality of bank staff, from both risk management and revenue efficiency perspectives. [ABSTRACT FROM AUTHOR]

This study investigates the impact of a company's environmental, social, and governance (ESG) activities on its financial performance. The COVID‐19 pandemic has caused unexpected difficulties in the operations of most companies, leading to a significant deterioration in their financial performance. We use empirical analysis to examine whether a company's ESG activities affect its financial performance. We employ the context‐dependent data envelopment analysis (DEA) model to evaluate the performance and provide a benchmark‐learning roadmap for 27 photoelectric companies in Taiwan. We also compare the marketability, innovation ability, and profitability of three sub‐industries: camera modules, optical component lenses, and optical components. Furthermore, we utilize a combination of classical DEA, stratification DEA, measurement of attractiveness, and progress for company evaluation. The results indicate that validating sustainability reports has a positive information effect, and high‐quality Global Reporting Initiative‐based external third‐party confirmed reports possess relatively high credibility, readability, and information value. The content and quality of these reports have an impact on performance. These findings offer insights into the role of ESG activities in the photoelectric industry and can help managers reflect upon and justify their choice to invest in specific ESG activities. Therefore, this research highlights the importance of green operations for firms. [ABSTRACT FROM AUTHOR]

A conceptual framework is constructed to discuss the Energy Transformation Efficiency and the Energy Transformation Justice for Carbon Neutrality and sustainable development. Both the Slack Based Measure‐based Efficiency Model and the improved Energy Transformation Justice Model are applied to measure the transformation efficiency and the transformation justice respectively. Then for empirical analysis, the influencing mechanism of the energy transformation efficiency and justice on the energy economy is analyzed by using the panel data of the USA, China and the EU from 2000 to 2020 with the Panel Smooth Transition Regression (PSTR). The results reveal that: The energy transformation efficiency requires a subtle balance between three essential inputs and two important outputs, while the energy transformation justice associated with five dimensions can describe both the equal opportunity and the procedural justice. Both the energy transformation efficiency and the energy transformation justice are improving year by year, and they are not completely mutually exclusive at present. As far as the performance is concerned, the EU is the best of all at both the efficiency and the justice, the USA is better than China at the justice, but China is better than the USA at the efficiency. In terms of interrelationship, there is an obvious Regime Switching Effect when the energy transformation efficiency and justice influencing on the energy economy growth. Furthermore, note that the state transition of both the efficiency and the justice can be limited by the economic situation of energy sector in each economy. [ABSTRACT FROM AUTHOR]

This research introduces a novel empirical application to the assessment of farm productivity growth. While the existing research on productivity change has primarily focussed on ex post output observations, it has been shown that ignoring production uncertainty can lead to unreliable results. Using a state‐contingent framework to represent the stochastic production environment, we extend the recent line of research that merged the state‐contingent approach and efficiency measurement to productivity change using the Malmquist and Luenberger productivity indices. Using a balanced panel of 117 arable crop farms surveyed in 2011 and 2015, we show through the study results that productivity decreased, with technological regress being the major source of productivity change. Differences in productivity change between nonstochastic and stochastic modelling show the relevance to consider the state‐contingent framework when assessing farms' productivity. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) model has been applied for evaluating bank branches and recognizing efficient and inefficient branches can help bank managers to provide appropriate strategies to improve the inefficient branches' performance. Conventional DEA models are based on the 'black box' approach. However, the process of providing services in banks is made up of interactive and interdependent processes. Additionally, some managers tend to incorporate their preferences in evaluation process. In this paper, best worst method (BWM) is used for incorporating decision maker (DM) preferences in two‐stage DEA model. First, BWM model is applied to obtain the weights of inputs, intermediate measures and outputs based on DM's judgement. Second, generated weights are imposed on two‐stage DEA model as additional constraints and a novel bi‐objective BWM‐two stage DEA model is introduced. Finally, the proposed bi‐objective BWM‐two stage DEA model is solved using min‐max approach. To illustrate the capability of proposed model, 45 Agricultural Bank (Agribank) branches in West Azerbaijan province of Iran are evaluated. The branches' processes are considered as two stages 'production' and 'profitability' and efficiency of branches are calculated in each stage. According to the efficiencies of each sub‐stage, branches are divided to four groups and recommendations are made for each group. [ABSTRACT FROM AUTHOR]