Data Envelopment Analysis Cross-efficiency Research Articles

The evolution of benchmaking the carbon efficiency drinking water companies in England and Wales

Water utilities face the challenge of transitioning to a low-carbon urban water cycle while reducing operational costs. This study evaluates the static and dynamic carbon efficiency of a sample of water companies from 2013 to 2018 operating in England and Wales. Each company was evaluated relative to itself and its peers using cross-efficiency Data Envelopment Analysis techniques. The results showed that the carbon performance of the water industry improved by 2.1% per year, mainly due to efficiency change. In contrast, the contribution of factors driving technical and scale change was almost negligible.

Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.

CROSS-BARGAIN-EEF EVALUATION APPROACH FOR CO2 EMISSION EFFICIENCY IN CHINA’S CONSTRUCTION INDUSTRY WITH FIXED-SUM UNDESIRABLE OUTPUT

The rapid urbanization in China has led to a substantial expansion of construction activities, causing notable energy consumption and a consequent rise in CO2 emissions. This study proposes an approach to assess CO2 emission efficiency in China’s construction industry by integrating data envelopment analysis cross-efficiency and the Nash bargaining game, considering CO2 as a fixed-sum undesirable output. The study identifies Jiangsu and Beijing as top performers, establishing benchmarks for other provinces. In contrast, Inner Mongolia displays the lowest efficiency. Generally, most provinces exhibit room to improve environmental efficiency in the construction sector. The study provides policy implications and tailored suggestions for specific provinces.

A Flexible Cross-Efficiency Model with Partial Preference for Efficiency Evaluation of Clean Transportation Energy

The rapid development of the transportation industry benefits from the consumption of energy, but the excessive dependence on petroleum fuels makes it a major source of air pollution. In order to achieve green and high-quality development of the transportation industry, many countries are committed to scientifically evaluating the utilization efficiency of clean energy, which has attracted wide attention from the whole society. Significantly, without considering the diversity and complexity of pollutants, indicators used in previous studies were unable to cover all pollutants when establishing the evaluation index system. Meanwhile, as an efficient tool, data envelopment analysis (DEA) is extensively used when it comes to efficiency evaluation. However, the absolute preference of existing benevolent and aggressive cross-efficiency models limits its application scenarios. To address the challenges above, an improved flexible cross-efficiency DEA model is proposed considering both same and different benevolence coefficients of decision-making units (DMUs) on the basis of pointing out the inadequacy of the previous model. The concepts of consensus coefficient and group preference are introduced in the aggregation of cross-efficiency. Besides, based on the theory of undesirable output, the consumption of nonclean energy is taken into account as the input indicator to characterize the degree of pollution. The results show that the obtained cross-efficiency value and efficiency ranking of clean transportation energy change sensitively under various benevolent coefficients. There is an important practical significance to consider the independent preference information of DMUs for the evaluation and ranking of cross-efficiency.

DEA cross-efficiency models with prospect theory and distance entropy: An empirical study on high-tech industries

Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations.

Cross-efficiencyBasedonGeneralized-DEAandItsApplication

Traditional DEA selects automatically inner Decision-Making Units (DMUs) as the reference set. However, some applications might need specified units. Based on generalized DEA and cross-efficiency DEA, a cross-GDEA model is constructed in this paper, which not only considers any specified reference set in efficiency evaluation but also ranks distinguishably all DMUs. A numerical example and comparative analysis are given to illustrate the reasonableness of the cross-GDEA model. Comparative analysis with several established DEA models reveals the superior flexibility and generality of our method. Furthermore, an application aiming at measuring the multiple-factor carbon productivity of Western China is given to verify the applicability of the cross-GDEA model. Results show the proposed approach can contribute to advancing the theoretical and practical research of DEA.

Logistics Efficiency Evaluation and Empirical Research under the New Retailing Model: The Way toward Sustainable Development

As an innovative retailing mode, “new retailing” is making the distinction between physical and virtual commerce obsolete, where offline stores attract customers and supply them with the opportunity of experiences, and online stores provide services for placing orders and transactions. However, many new retailing companies are beset by their logistics efficiency evaluations because evaluation indicators and methods differ significantly from those with traditional business patterns. In this way, considering the sustainable development principle, this study focuses on the approach of logistics efficiency evaluation and empirical analysis under the new retailing model, explores the main factors related to logistics efficiency improvement, and proposes corresponding measures to reinforce the core competence of companies. We first establish a novel logistics efficiency evaluation index system by word frequency and correlation analysis from a sustainable development view, including six first-level indicators and twenty-seven second-level indicators; then, the logistics efficiency evaluation framework is presented: the static evaluation is made using cross-efficiency DEA and IAHP entropy; the dynamic evaluation is performed using the Malmquist index. After that, a demonstrated analysis of logistics efficiency is conducted with 15 listed companies in China. Furthermore, efficiency-influencing factors are identified using Tobit regression, and countermeasures are proposed to promote the development of new retailing. Comparison results show that the proposed logistics efficiency evaluation framework can be scientific and reliable, helping managers to identify the weaknesses and influencing factors in enterprise logistics operations, therefore improving the performance and competitiveness of new retailing companies.

Fuzzy cross-efficiency evaluation based on prospect theory and regret theory

Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper’s method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper’s method is verified by comparing three traditional methods with this paper’s method, which provides an effective method for considering risk preferences in the decision-making process.

Spatiotemporal evolution and trend prediction of regional water-energy-food-ecology system vulnerability: a case study of the Yangtze River Economic Belt, China.

The vulnerability study of the water-energy-food-ecosystem (WEFE) system in the Yangtze River Economic Belt (YREB) is of great significance for ensuring water, energy, food, and ecological security. In this study, the concept of vulnerability of WEFE systems is explained, and then its mechanism is analyzed. Based on the vulnerability concept of the WEFE system and combined with the pressure-state-response (PSR) framework and the exposure-sensitivity-adaptability (ESA) capacity model, this study constructs an evaluation index system for the vulnerability of the WEFE system. The cross-efficiency data envelopment analysis (CE-DEA) method, which considers both self-evaluation and peer evaluation efficiency, is used to calculate the vulnerability from 2005 to 2020. The spatiotemporal dynamic characteristics are explored using the hot spot analysis and spatial autocorrelation model. The results show that the overall vulnerability of the WEFE system in the YREB has fluctuated and increased during the study period, with a spatial pattern characterized by "high in the middle and low on both sides." Over time, the spatial evolution tends to be centralized and non-equilibrium, forming a relatively independent spatial pattern.

Heterogeneous multi-attribute group decision making based on a fuzzy data envelopment analysis cross-efficiency model

Heterogeneous multi-attribute group decision making (HMAGDM) is a kind of complex decision making problem, which exists widely in reality. The purpose of this paper is to extend data envelopment analysis (DEA) for solving the HMAGDM problems whose attribute values include real numbers, intervals, intuitionistic fuzzy numbers, triangular fuzzy numbers, trapezoidal fuzzy numbers and linguistic variables. For this purpose, a method to HMAGDM based on a fuzzy DEA cross-efficiency model is proposed. In the proposed method, heterogeneous information is uniformly transformed into intervals, which are expressed as variables with unknown parameters. On this basis, a fuzzy DEA cross-efficiency model is established to calculate the individual cross-efficiency intervals of decision making units (DMUs). The model takes into consideration all possible attribute values of each DMU in the intervals, and obtains stable evaluation results through peer evaluation mode and iteration. Then, a maximizing group consensus model is established to calculate the weights of decision makers (DMs). Given the weights of DMs, the individual cross-efficiency intervals can be aggregated into the collective cross-efficiency interval, and DMU can be ranked based on this. An example of supplier selection is presented to illustrate the practicability and effectiveness of the proposed method.

Cross efficiency model of network DEA and its application on low carbon efficiency evaluation of multimodal transport

The transportation industry is the main source of carbon dioxide (CO2) emissions, and the development of multimodal transport is conducive to reducing transportation carbon emissions. In order to evaluate the low carbon efficiency of multimodal transport, this article proposes a cross efficiency data envelopment analysis (DEA) method for complex network structures from the perspective of fairness, and takes China's rail-water intermodal transport as an example for research. The proposed method includes two secondary goal models that reduce the unreasonable differences between self-evaluation and peer-evaluation and treat all stages within the network system equally. Then, the improved entropy weight method is used to aggregate the cross efficiency. In addition, a numerical example is given to verify the effectiveness of the proposed method. The results of the case study show that the efficiency of Tangshan Port and Rizhao Port is relatively high, and others should learn from the successful experiences and develop improvement strategies based on their own characteristics. The conclusions can provide reference for transportation enterprise management and policy formulation, and the proposed method can be widely applied to the performance analysis of enterprise network, industrial chain, and supply chain.

Group decision-making with multiplicative probabilistic linguistic preference relations based on consistency improvement and upgraded multiplicative DEA cross-efficiency

Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach.

A dynamic network data envelopment analysis cross-efficiency evaluation on the benefits of bus transit services in 33 Chinese cities

ABSTRACT Our objective in this paper is to evaluate the benefits of bus transit. To obtain the more accurate and highly discrimination results, this study establishes an evaluation indicator system from a stakeholder perspective and proposes a novel combined method consisting of dynamic network data envelopment analysis, cross-efficiency evaluation, and Shannon entropy aggregation method. An empirical study of bus transit systems in 33 key Chinese cities from 2016 to 2019 is provided. The results show that the benefit of bus transit in most key cities was ineffective. In contrast to production efficiency, focusing on improving service effectiveness is more conducive to improving the benefits of bus transit. Small cities have higher benefits than large cities. Meanwhile, the benefits of bus transit vary geographically. This evaluation outcome provides a more appropriate basis for decision-making related to the benefits of public transit services, as well as for operational and management studies.

Efficiency Evaluation and Influencing Factors of Sports Industry and Tourism Industry Convergence Based on China’s Provincial Data

As sunrise industries, green industries and happiness industries, the sports industry and the tourism industry have natural convergence, and the convergence of the sports and tourism industries is not only a new strategic support point for the sustainable development of the two industries but also an important way to drive healthy and sustainable development of related industries. We measure the efficiency of sports–tourism convergence in 31 provinces (municipalities and autonomous regions) in China from 2013 to 2019 using the benevolent cross-efficiency DEA model and the qualitative comparative analysis (fsQCA) method and conduct a configuration analysis of the influencing factors. The research indicates: (1) the efficiency of the development of sports–tourism convergence is low and its role in the sustainable development of the tourism and sports industries needs to be improved. (2) The efficiency of sports–tourism convergence shows spatial distribution characteristics of decreasing efficiency in the eastern region–central region–western region. (3) Industrial structure, transportation conditions, openness to the outside world, political support and their combination are the core driving factors affecting integration of sports and tourism.

On a Choquet Integral DEA Cross-Efficiency Evaluation Method Involving the Satisfaction of Decision-Making Units and Weight Balance of Indicators

The secondary goal approach is an effective way to address the problem of the non-uniqueness of the optimal weights The secondary goal approach is an effective way to address the problem of the non-uniqueness of the optimal weights for decision-making units (DMUs) in the cross-efficiency evaluation process of data envelopment analysis (DEA). However, on the one hand, existing secondary-goal models seldom consider the willingness of DMUs to accept and be satisfied with the cross-efficiency evaluation results of DEA. On the other hand, the problem of zero weights and excessive differences of indicators has not been given enough attention. Furthermore, the traditional DEA approach ignores interactions between indicators. To overcome these problems, in the context of considering the interaction of input (or output) indicators, this study introduces the idea of the minimum dissimilarity of weights and constructs an improved method for evaluating the cross-efficiency of the secondary goal based on the DMUs’ satisfaction. In this method, the 2-additive Choquet integral is used as a more feasible attempt to reflect the pairwise interaction between input (or output) indicators to improve the differentiation of DEA evaluation results. Additionally, the satisfaction targets of DMUs are adjusted according to the three ethical principles of fairness (fraternity), utilitarianism and equity in social choice theory to obtain a choice of optimal weights that is consistent with the values of individual DMUs. This paper validates the performance of the improved DEA method through comparative analysis of arithmetic cases. Then the method is applied to the study of the input–output efficiency assessment of the water–energy–food nexus in China.

Provincial Inclusive Green Growth Efficiency in China: Spatial Correlation Network Investigation and Its Influence Factors

Inclusive green growth efficiency (IGGE) analysis is an effective tool for improving coordinated economic, social, and environmental development. This study incorporated the game cross-efficiency DEA to measure the IGGE of 30 provinces in China. Then, the modified spatial gravity model and social network analysis model were applied to construct and analyze the spatial correlation network structure of the IGGE. The quadratic assignment procedure was used to mine the influencing factors that affect the formation and evolution of the spatial correlation network of the IGGE. The results are as follows. (1) During the study period, there were significant differences in the IGGE among the 31 provinces, among which the eastern provinces were higher than the central and western provinces. (2) The spatial correlation of the IGGE presented a complex and multi-threaded network structure, indicating that the IGGE has a noticeable cross-regional spillover effect. Beijing, Tianjin, Zhejiang, Shanghai, Jiangsu, and Guangdong played the role of the “net spillover” block. Qinghai, Guizhou, Guangxi, and the surrounding provinces played the role of the “primary beneficial”. The Yangtze delta and Pearl River Delta economic zone (primarily including Shanghai and Guangdong) acted as a “bridge” to the Yunnan–Guizhou region and the surrounding provinces. (3) The spatial adjacency, degree of openness, economic development, and environmental governance were the prominent factors influencing the formation and evolution of the IGGE spatial correlation network. This work provides an example of constructing an IGGE correlation network while considering various factors, such as the economy, population, and distance. It also could help policymakers clarify the IGGE spatial correlation pattern and the provinces’ roles and potential for IGGE synergic improvement.

Global Inclusive and Sustainable Competitive Industrial Performance (ISCIP): An efficiency based spatio-temporal analysis for 139 countries

The most recent debate championed by World Economic Forum suggests that competitiveness, social cohesion and environmental sustainability may be compatible. This gives hope to policymakers that a productive, low-carbon, and inclusive economy is possible and perhaps the only viable option going forward. In the context of industrial development, a diagnostics and monitoring tool that can benchmark a country’s industrial competitiveness within the broader framework of the post-2030 agenda is therefore highly sought by policymakers. The original contribution of the present study is to expand the scope of United Nations Industrial Development Organization’s Competitiveness Industrial Performance Index by proposing the Inclusive and Sustainable Competitive Industrial Performance Index which (i) considers a new set of social and environmental dimensions in addition to the industrial competitiveness; (ii) provides the flexibility of a dual-formulation of manufacturing-specific and whole-economy scenarios to reflect different policy needs of countries; (iii) applies a cross-efficiency Data Envelopment Analysis model and a spatio-temporal analysis to optimize the composition of Inclusive and Sustainable Competitive Industrial Performance Index indicators for 139 countries (2010–2019). With a different method of aggregation and new dimensions being taken into account, results concerning the Competitive Industrial Performance Index and the Inclusive and Sustainable Competitive Industrial Performance Index country rankings are aligned in flagging that industrial competitive and high-income countries are more effective than their peers in promoting industrial development by minimizing tradeoffs with social and environmental indicators. A limitation of the present study is that results do not reflect the global shocks affecting world megatrends after 2019. Second, as the Inclusive and Sustainable Competitive Industrial Index remains a composite indicator, a high rank can still hide significant negative externalities in absolute terms. Finally, the set of the composing ISCIP sub – indicators cannot be exhaustive to capture all the aspects of sustainability.

Evaluation of the integration of industrialization and information-based entropy AHP–cross-efficiency DEA model

PurposeThe purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.Design/methodology/approachThis study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.FindingsThe data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.Originality/valueBy adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

Regret-rejoice two-stage multiplicative DEA models-driven cross-efficiency evaluation with probabilistic linguistic information

As a powerful efficiency evaluation tool, cross-efficiency measurement in data envelopment analysis (DEA) has been proverbially concerned. However, most of existing studies on cross-efficiency fail to notice the internal processes of decision-making units (DMUs) and the psychological behaviors of decision makers (DMs) simultaneously. To overcome this limitation, this study investigates the rejoice-regret probabilistic linguistic multiplicative DEA cross-efficiency evaluation method for two-stage system. First, considering the increasingly complex decision-making environment, probabilistic linguistic term set is employed to express inputs and outputs information, and a probabilistic linguistic multiplicative DEA (PLMDEA) model is designed. Then, the regret theory is adopted to construct a regret-rejoice cross-efficiency PLMDEA (RC-PLMDEA) model, in which the regret attitude of DMs is captured. Subsequently, as the internal process of each DMU is remarked, a regret-rejoice cross-efficiency two-stage PLMDEA (RCTS-PLMDEA) model is established. In the end, we provide an empirical application of evaluating commercial banks for examining the effectiveness and applicability of the designed method, and sensitive analysis and comparative analysis are also offered to highlight the merits and robustness of the constructed method.

Domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis

Although university technology licencing has been the subject of many studies, a major gap remains in the literature regarding ways to assess the economic value of university technologies. We propose an analytical framework for the domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis (DEA). First, 18 potential quantitative indicators of the economic value of university technologies are extracted from technology transaction, patent, and publication databases using bibliometrics. Second, given the heterogeneity across technology fields, significant indicators suited to a technology field of interest are identified using the Jonckheere–Terpstra tests. Third, a composite indicator is developed as a proxy for the economic value of university technologies using the DEA cross-efficiency method. Finally, the validity and utility of the analytical framework are examined using correlation analysis and the Jonckheere–Terpstra test. Accordingly, we explore the different implications of quantitative indicators in the valuation of university technologies across technology fields. A case study of the technologies registered in the Office of Technology Licensing at Stanford University confirms that the proposed analytical framework is valuable as a complementary tool for the valuation of university technologies.