DEA Model Research Articles

Evaluation of Capital Utilization Efficiency in Agricultural Listed Firms Based on a Three-Stage Data Envelopment Analysis

Rural revitalization is intricately tied to national prosperity, with listed companies in agriculture, forestry, animal husbandry, and fisheries playing pivotal roles in driving this transformative agenda. However, these sectors face critical challenges, notably resource constraints and environmental pressures, which underscore the necessity of evaluating capital utilization efficiency. This study employs a combined three-stage DEA model and Malmquist index to analyze capital utilization efficiency across 85 listed companies within China’s agriculture, forestry, animal husbandry, and fishery industries over the period 2018–2022. The primary objectives are to quantify capital utilization efficiency, identify pathways for industry optimization, advance ecological agricultural sustainability, and offer valuable insights to investors and policymakers. Key findings include: (1) When grouped by sub-sector and adjusted to account for external environmental factors and random disturbances, the initial comprehensive capital utilization efficiency in agricultural companies was found to be significantly overestimated. Subsequent third-stage adjustments revealed decreases in average comprehensive efficiency, technical efficiency, and scale efficiency by 29.79%, 3.03%, and 27.37%, respectively, largely driven by a marked decline in scale efficiency, ultimately diminishing overall efficiency. (2) The capital utilization efficiency in agricultural trading firms remains suboptimal, with scale efficiency posing a critical limitation. High dependency on government subsidies and excess specialized personnel further constrain efficiency improvements. (3) Dynamic analysis using the DEA-Malmquist model indicates low total factor productivity in the agricultural sub-sector, primarily due to inefficiencies in capital management and suboptimal scale allocation. These findings underscore the need for targeted strategies to enhance resource allocation and management, bolster talent development and financial management frameworks, and drive technological research and development, innovation, and efficient capital allocation across the agriculture, forestry, animal husbandry, and fishery sectors.

The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling

This research examines the energy efficiency of city districts through the Malmquist–DEA model and investigates the spatial effects of the service trade and the digital economy on energy efficiency in urban green development. The study also delves into the specific context of the AI service trade to gain insights into and align with the emerging digital intelligence industry. The interplay of the service trade with the digital economy, alongside the AI service trade with innovation, significantly enhances urban energy efficiency and demonstrates positive externalities. Building on the empirical findings, this research employs cluster analysis to explore the green development of city districts and uses AI technology to program green communication and cooperation mechanisms across district clusters, employing quantum computation based on QUBO modeling. This study contributes to a deeper understanding of the cofunction of the service trade and the digital economy in terms of energy efficiency and aids in developing new quality productivities for green cities through quantum-based AI advancements. This research has clear implications for cutting-edge interdisciplinary applications of green AI technologies in social computing science.

Optimizing Supply Chain Efficiency Using Cross-Efficiency Analysis and Inverse DEA Models

Our study employs cross-efficiency analysis (CEA) and machine learning techniques to optimize supply chain performance. By integrating inverse DEA models with directional distance functions, we measure operational efficiency across various decision-making units (DMUs), accounting for undesirable outputs such as excess costs and emissions. Our results indicate a 20% improvement in market recognition efficiency and a 15% increase in earnings persistence efficiency after model application. Additionally, machine learning classifiers, including Random Forest and Support Vector Machines, further enhanced predictive accuracy, with Random Forest achieving the lowest mean absolute error of 0.07. These findings underscore the effectiveness of advanced analytical models in improving supply chain resilience and decision-making accuracy, contributing to sustainable operational performance.

A Modified Network DEA Model for Bank Efficiency Analysis Considering Risk Factors

A Modified Network DEA Model for Bank Efficiency Analysis Considering Risk Factors

Evaluating the influence of environmental regulations on green economic growth in China: A focus on renewable energy and energy efficiency guidelines

China's status as the world's largest energy consumer highlights its pivotal role in global energy consumption and the urgency of enhancing its energy efficiency and renewable energy development. This study evaluates the influence of environmental regulations on green economic growth in China, with a focus on renewable energy and energy efficiency, from 2000 to 2023 across 30 provinces. Using the Metafrontier-Global-SBM super-efficient DEA model, the research measures green economic development through the lens of energy efficiency. An enhanced fuzzy comprehensive assessment gauges the stringency of environmental regulations. To explore non-linear impacts, regional spillover, and feedback effects, a spatial econometric model was applied. The results reveal a U-shaped non-linear relationship between environmental regulations and green economic growth, with significant spatial feedback and regional spillover effects. Environmental regulations are also shown to have a substantial positive influence on the development of renewable energy, a critical driver of green economic growth. Policy implications suggest that strengthening environmental regulations could accelerate renewable energy adoption and foster sustainable economic growth across regions in China.

On the efficiency of US community banks around the COVID-19 outbreak

ABSTRACT This paper examines how the COVID-19 pandemic affected the efficiency of 569 publicly traded community banks in the United States. Using a slacks-based measure (SBM) DEA modelling framework, we analyze quarterly data around the pandemic outbreak to estimate overall technical efficiency, pure technical efficiency, and scale efficiency scores. We use four assessment perspectives: general, loan-volume focused, loan-income focused and income-focused models. Additionally, we incorporate credit risk adjustment in loan and income-focused models. Our findings indicate that U.S. community banks enhanced their efficiency during and after COVID-19, surpassing pre-pandemic levels. Managerial efficiency drove this improvement. Adjusting for default risk weakened efficiency improvements, revealing a more accurate picture of efficiency changes during and after the pandemic.

A study on the operational and competitive efficiency of National Oil Companies using two-stage network DEA model

A study on the operational and competitive efficiency of National Oil Companies using two-stage network DEA model

Research On The Operational Efficiency And Development Mode Of Airport Clusters

Airports are an important support for building a strong civil aviation country, and the development of airport clusters is of paramount importance in accelerating the development of civil aviation. The paper selects the Yangtze River Delta Airport Clusters, Beijing Tianjin Hebei Airport Clusters, Pearl River Delta Airport Clusters, Chengdu Chongqing Airport Clusters, Paris Airport Clusters, Berlin Airport Clusters, Frankfurt Airport Clusters, New York Airport Clusters, Washington Airport Clusters, Chicago Airport Clusters, and Los Angeles Airport Clusters as research objects. The operational data and input-output data of the sample airport Clusterss are collected, and the DEA model is used to evaluate the operational efficiency of the sample airport clusterss. The differences in operational efficiency of airport clusterss under different development models are compared, and the conclusion is that the operational efficiency of multi center airport clusterss is generally better than that of single center airport clusters

Assessing performance of water supply operation of water companies in China: A union dynamic multi-activity network data envelopment analysis

Urban growth and the increasing urban water demand highlight the importance of performance analysis in the water supply industry. Both water supply companies (WSCs) and water supply and sewage companies (WSSCs) produce and deliver potable water for city dwellers. Water authorities use benchmarks to monitor the two types of companies’ operations. The internal operation process and heterogeneities of observations were not considered for relative performance evaluation by conventional DEA models. To overcome these limitations, this study proposes a union dynamic multi-activity network data envelopment analysis (DMANDEA) model to extend static network DEA to a more general case for dynamic efficiency assessment of the water supply operations of water companies. For the first time, the model allows for the decomposition of the water supply operation into water production (WP) and water consumption (WC) substages and performance comparisons for water companies by considering their heterogeneity simultaneously. Specifically, by using a non-radial directional distance function (DDF), the inefficiencies for substages and the entire system can be both assessed dynamically. This model is applied to a sample of 246 water companies in China in 2016–2018. The overall, period, and substages’ group efficiencies, meta efficiency, and technology gap ratio (TGR) for 179 WSCs and 67 WSSCs are all provided. The empirical results reveal that: (1) the average group efficiencies of WSCs and WSSCs are 0.4567 and 0.6611, respectively. The meta efficiencies of WSCs and WSSCs are 0.4523 and 0.4703, respectively. The TGR of WSCs (0.994) is greater than that of WSSCs (0.7067). (2) WSSCs perform better than WSCs do, which suggests that economies of scope do exist in WSSCs. However, WSCs possess the best technology for water supply operations because of their high TGR. (3) The average meta efficiency for all the observed water companies (0.4572) is not optimistic, which correlates more with inefficiency in the WP stage. The causes stem from the natural monopoly, lack of competition, and relatively lower water tariffs. The Chinese water supply industry requires diversification of ownership and operation modes, and gradual increases in water tariffs. (4) Water companies in the eastern region outperform those in other regions. Regional cooperation, such as technology sharing, the transfer of managerial experience, and information exchange, needs to be emphasized.

How to evaluate the efficiency of rural preschool education resources and its regional differences in China

Rural preschool education is an integral part of rural society, and improving the efficiency system of evaluation of rural preschool education resource allocation is an important strategy for the implementation of the rural revitalization. This paper uses an input-oriented three-stage DEA model to analyze the efficiency of rural preschool education resource allocation in 30 provinces in China from 2012 to 2020. The results show that external factors such as the level of urbanization, birth rate, and the scale of kindergarten impacts the efficiency of rural preschool education resource allocation significantly. Without regard to the influence of environmental and random factors, the overall trend of the average efficiency of rural preschool education resource allocation in China has improved, showing a regional pattern of “central > eastern > western.” Therefore, based on the relevant policies, this paper puts forward rational suggestions for the improvement of rural preschool resource allocation efficiency in China from the perspectives of human, financial and material resources.

Does embeddedness in strategic alliances matter for innovation efficiency? The moderating roles of government R&D subsidies and standardization

PurposeThis paper investigates the impact of strategic alliance network centrality and structural holes on firm innovation efficiency. In addition, the paper aims to explore the moderating effects of government R&D subsidies and firm technology standardization.Design/methodology/approachBased upon the literature on strategic alliance networks, this paper proposes a conceptual model with several hypotheses. The empirical analysis is based on a sample of 736 observations from 92 mechanical manufacturing firms in China from 2010 to 2017. We measured firm innovation efficiency via the DEA model and performed quantitative analysis with GMM estimation.FindingsThe results indicate that strategic alliance network centrality is positively related to firm innovation efficiency, and structural holes have a U-shaped relationship with firm innovation efficiency. Government R&D subsidies positively moderate the relationship between centrality and firm innovation efficiency. Firm technology standardization positively moderates the relationship between centrality and firm innovation efficiency and the U-shaped relationship between structural holes and firm innovation efficiency.Practical implicationsFirms should focus on improving innovation efficiency and maximizing innovation output under limited resources. Furthermore, managers ought to strengthen cooperation between firms and external alliances while promoting the utilization of strategic alliance network position resources to benefit innovation efficiency.Originality/valueThis paper considers that innovation efficiency, including input and output processes, is more representative than innovation performance, and few studies have focused on the relationship between strategic alliance networks and innovation efficiency. To fill this research gap, this paper explores the impact of strategic alliance network position embedding on innovation efficiency.

Measurement and spatial and temporal characterization of logistics efficiency--Based on a three-stage DEA model

The logistics industry is an essential component of economic and social development. Enhancing its efficiency contributes to national economic progress. However, the sector faces challenges such as high costs and pollution. This paper conducts an empirical study on the logistics efficiency of 30 Chinese provinces (excluding Xinjiang Uygur Autonomous Region) from 2004 to 2021 using a three-stage DEA model. Findings reveal that before adjustment, five provinces, including Beijing and Shanghai, were on the efficiency frontier, reduced to three after adjustment. Other provinces exhibit room for improved logistics efficiency, particularly in addressing cost and pollution issues. The key to enhancing efficiency lies in technical and scale efficiency adjustments. External environmental factors significantly influence logistics efficiency; social retail goods consumption positively correlates, while Gross Regional Product negatively correlates. Overall, China's logistics efficiency displays an upward trend but with notable regional disparities, indicating higher efficiency in the eastern region and lower efficiency in central and western regions, resulting in an overall "high in the east, low in the west" pattern.

Assessing airline efficiency with a network DEA model: A Z-number approach with shared resources, undesirable outputs, and negative data

This study measures the efficiency of airlines using a novel fuzzy common weight additive network data envelopment analysis (NDEA) with shared resources, negative data, and undesirable outputs. First, an appropriate two-stage network is designed for each airline so that stages 1 and 2 are called the Production and Service stages, respectively. The proposed model adopts a top-down approach and calculates the efficiency of the system first and then estimates the efficiency of stages 1 and 2. To evaluate and predict the airlines’ efficiency considering fuzzy data and the reliability of the information, the values of input/intermediate/output variables are predicted as the Z-number and the appropriate Z-number version of NDEA (ZNDEA) models is proposed. To develop the proposed ZNDEA models and find common weights for the variables, three multi-objective ZNDEA models for the system, stage 1 and stage 2 are presented. The multi-objective common weight ZNDEA models are solved using the min-max Chebyshev goal programming technique and the final efficiencies are calculated. To illustrate the capability of the proposed approach, real-life data from Iranian airlines in 2022 are collected, and the efficiencies are analyzed.

Relative efficiency diagnosis of overseas construction projects by construction type using the DEA model

Relative efficiency diagnosis of overseas construction projects by construction type using the DEA model

Measurement and Spatial-Temporal Evolution of Industrial Carbon Emission Efficiency in Western China

As it is an important industrial base in China, it is of great significance to improve the industrial carbon emission efficiency in the western region to promote the low-carbon sustainable development of the region. This paper selects the input–output panel data of 11 provinces in western China from 2010 to 2021, and adopts the three-stage DEA model to measure the industrial carbon emission efficiency in western China under a non-traditional geographic division at the overall and regional levels and analyze its influencing factors. The Dagum Gini coefficient, its decomposition method, and the kernel density estimation method are used to analyze the regional differences and dynamic evolution process of industrial carbon emission efficiency in the western region. The results of the study show that (1) after removing environmental and random factors, the industrial carbon emission efficiency in western China has been improved, but there are inter-regional differences, characterized by “the third region > the second region > the first region”; (2) the levels of green development, shared development, innovative development, and coordinated development have a positive impact on the improvement of industrial carbon emission efficiency in western China, while the level of industrialization has a relatively smaller influence, and economic development, government support, open development level, and energy consumption structure have not yet played a significant role; (3) the spatial differences in the efficiency of industrial carbon emissions in western China have generally increased during the sample period, with inter-regional differences being the main source; and (4) the industrial carbon emission efficiency in western China is characterized by overall improvements in time and space but with stage differences and multi-polarization of regional differences. This study has a certain reference value for improving industrial carbon emission efficiency in western China.

Research on Impact of Design Innovation Factors on Pure Technical Efficiency of Manufacturing Innovation

Context: Improving the pure technical efficiency in manufacturing innovation is crucial to achieving sustainable development in the manufacturing industry. Objective: We aimed to explore the impact of design innovation factors on the pure technical efficiency of manufacturing innovation from 2011 to 2021, with industrial enterprises above the designated size in Zhejiang Province, China, taken as the research object. Method: The super-efficiency DEA model was used to calculate the pure technical efficiency of manufacturing innovation. Literature research, combined with the Pearson correlation coefficient, was employed to obtain five design innovation factors, including the number of policy regulations, growth rate of designers, number of design enterprises, number of patents granted, and number of design awards at the provincial level or above. Results: Based on the Tobit model, the influence of design innovation factors on the pure technical efficiency in manufacturing innovation was analyzed and demonstrated. Except for the number of policy regulations and growth rate of designers, the other three factors had a significant positive impact on the pure technical efficiency of manufacturing innovation. In general, design innovation exerts positive effects on the growth of pure technical efficiency. Conclusions: The results of this study provide helpful insights into the promotion of sustainable development in the manufacturing industry through design innovation.

Green transformation and performance synergy efficiency of china’s thermal power enterprises on the basis of the environmental tax burden

Global climate change and the collection of environmental protection taxes are accelerating the green transformation of thermal power enterprises. This study selected Chinese thermal power listed companies as samples and used a dynamic three-stage (operational, green transformation, and market performance) network DEA model to evaluate their transformation efficiency and corporate performance. This paper incorporates targeted indicators such as ESG (environment, society, governance) and stock prices into the model and conducts a comparative study on the basis of macro policies and the geographical location of the enterprise. A comparative analysis was conducted on the efficiency of enterprises before and after the adjustment of the environmental tax burden, using the environmental tax burden as an exogenous variable. Thus, the following conclusions can be drawn: there is a certain positive correlation between the collaborative efficiency of the two links of thermal power enterprises and the economic development of their respective regions. Moreover, the green transformation efficiency of most thermal power enterprises is superior to the market performance efficiency. The environmental tax burden mainly improves the overall efficiency of thermal power enterprises by improving their operational efficiency and efficiency in the green transformation stage without affecting market performance. To further improve efficiency, thermal power enterprises should actively communicate with stakeholders to strive for more financial relief.

Evaluation of market transformation efficiency of urban investment and development companies based on the three-stage DEA model

Evaluation of market transformation efficiency of urban investment and development companies based on the three-stage DEA model

G20 countries’ progress on the 7th SDG under circular economy DEA model

The purpose of this paper was to analyze the performance of the G20 nations in terms of efficiency and productivity concerning achieving the seventh Sustainable Development Goal (SDG 7) of providing affordable and clean energy between 2010 and 2019 under a circular economy framework. The diagnostic tools to measure included the output-oriented Data Envelopment Analysis (DEA, SBM) to determine efficiency and the Malmquist Productivity Index (MPI) to measure productivity. In the DEA model, renewable energy is the primary energy source as inputs and outputs as total Greenhouse Gases (GHG) emissions, electricity consumption, and renewable energy consumption. The results indicated that the efficiency and productivity of developed and emerging countries towards the 7th SDG targets were heterogeneous throughout the analysis. The results showed that among the G20 countries that ranked as efficient were emerging countries (South Africa, Brazil, India, and China). Developed countries have more to gain in efficiency by designing strategies with sustainable development and environmental policies consistent with CE actions than emerging countries. In addition, since productivity gains increased more due to efficiency changes than technology gains, as indicated by the MPI analysis, it seems reasonable to infer that strengthening research and development of resource-efficient technologies could help to reach the 7th SDG faster by increasing productivity through adopting new technologies. In the short run, all countries could benefit from a multilateral effort to reduce climate change and GHG emissions if developed countries provided technological support to emerging countries and the latter offered carbon credits to the former in exchange for measures to mitigate the emissions resulting from their rapid economic growth driven by capital investment.

Is There Gap between Operating Performance of Systemically Important and Non-Systemically Important Banks in China?--Empirical Study based on Public Panel Data after Release of List

On September 22, 2023, the People’s Bank of China (PBOC) and the State Administration for Financial Regulation (SAFS) released the latest list of China Systemically Important Banks (D-SIBs). This study aims to analyze whether there is a gap in operational performance between (D-SIBs) and (D-SIBs) under additional regulation. The research method uses independent sample t-tests in statistics and the AHP_DEA model for financial bank operational performance. The research results indicate that there are differences in the operational performance of systemically important banks and non-systemically important banks in certain indicators. systemically important banks have a larger share in the entire banking system, and systemically important banks face more regulatory constraints than non-systemically important banks. This makes the cost of capital restructuring for systemically important banks higher, thereby reducing the speed of capital restructuring. However, further analysis indicates that there is no significant difference in operational performance and risk control between banks with systemic importance and nonsystemic influence. In view of this, systemically important banks must invest in technology and innovation to improve operational efficiency.