Data Envelopment Analysis Model Research Articles

Efficiency assessment of public sector management and culture-led urban regeneration using the enhanced Russell-based directional distance function with stochastic data

Generic data envelopment analysis (DEA) models are based on deterministic input and output. However, input and output vectors are often interrupted by random factors, such as measurement errors and data noise, in real economic situations. This study proposes a new chance-constrained network DEA model based on the modified directional distance function (DDF) and enhanced Russell measure (ERM) model for assessing government management and culture-led urban regeneration. In addition to exploring the randomness of data, this study integrates the advantages of both ERM and DDF and considers the inefficiency level from a non-oriented viewpoint, the direction vector, and each input and output simultaneously. Each input and output of the two production stages can use non-radials to measure efficiency. Results show that the urban-rural gap has gradually widened since 2015. To validate the legitimacy of the model, this study utilizes the bootstrapping method to verify the results of the stochastic network DEA model and the conventional two-stage network DEA approach. This study also considers different alpha values as basis for comparison to confirm whether the results obtained differ by uncertainty level.

A parallel data envelopment analysis and Malmquist productivity index model of virtual frontier for evaluating scientific and technological innovation efficiency at universities

As an important component of the national innovation system, universities have received widespread attention in recent years regarding their Scientific and Technological Innovation (STI) efficiency. This study considers a new perspective for viewing talent cultivation, scientific research, and social service as parallel subprocesses for universities to perform their functions. We also combine the concept of virtual frontiers to calculate the STI efficiency at universities, avoiding the situation where multiple Decision-making Units (DMUs) cannot distinguish their rankings due to their effectiveness. In addition, we use the virtual frontier method to calculate the Malmquist Productivity Index (MPI), which avoids the inability of DMU to distinguish the Technical Efficiency Change (TEC) due to continuous period effectiveness. Through empirical analysis, we show (1) Although there are significant differences in STI efficiency among universities, it has gradually increased overall, rising from 0.1623 in 2014 to 0.2433 in 2018; (2) The overall MPI of universities is not particularly ideal, with two periods having an MPI less than 1, and only 14 universities having an average MPI exceeding 1 in four periods; (3) The overall average value for TEC is 1.1342, with only one university having a mean value below 1. On the other hand, the overall average for Frontier Shift (FS) is 0.8775, and all universities have FS values below 1. Finally, based on the research results, several practical suggestions are provided.

The convergence mechanism and spatial spillover effects of urban industry-university-research collaborative innovation performance in China

ABSTRACT This study deconstructs the triple helix structure of urban industry-university-research collaborative innovation into a two-stage chain structure of knowledge innovation and technological innovation. Based on data of 285 China prefecture-level cities from 2004 to 2018, this study measures the urban industry-university-research collaborative innovation performance using the network data envelopment analysis model. Additionally, it clarifies its spatial convergence mechanism and spatial spillover effects using the spatial convergence test and spatial Durbin model. The results suggest that: (1) the performance of China's urban industry-university-research collaborative innovation at different stages shows a spatial pattern with obvious regional differences and diversified development modes. (2) The overall performance shows a trend of gradual σ convergence with a slightly fluctuating rate, and the gap is constantly decreasing. Additionally, it shows an absolute β convergence trend at different stages, with an obvious geospatial club convergence trend in the eastern, central, and western regions. (3) The spatial spillover effects of ‘research-to-production’ performance and overall performance are stronger than that of ‘university-to-research’ performance. Finally, the collaborative innovation performance of various cities is dominated by the polarisation effect, and the its role as the driving force of regional innovation needs to be strengthened.

Coupling coordination degree of industrial solid waste prevention and treatment efficiencies and its driving factors in China

Both prevention and treatment are crucial for reducing industrial solid waste (ISW). The primary objective of this study is to explore the coupling coordination degree (CCD) of China’s provincial ISW prevention and treatment from the perspective of efficiency analysis during 2011–2020 and then investigate its driving factors in three steps. The first step is to develop a base point network slacks-based measure data envelopment analysis model to address the issue where the indicators (ISW discharged, ISW stored) of some regions are zero during the evaluation of the ISW prevention and treatment efficiencies. Secondly, the CCD model is constructed to investigate the CCD of ISW prevention and treatment efficiencies. Finally, the driving factors of the CCD are explored using the spatial Tobit model. The results show that: (1) In 76.67% of regions, the prevention efficiency is higher than treatment efficiency, and in 23.33% of regions, the treatment efficiency is higher than prevention efficiency. The prevention efficiency of the entire country exhibits a steady downward trend, while the treatment efficiency, on the whole, is increasing. (2) The regions with high coordination are mainly situated in the east, those with moderate coordination are primarily located in the central region, and those with basic coordination are mostly located in the west. Only three regions (Jiangxi, Guangxi, Shaanxi) exhibit moderate imbalance. (3) The improvement of CCD mainly depends on industrial servitization and industrial structure, while property structure, ISW treatment investment intensity, and traffic infrastructure construction significantly impede the increase of the CCD.

Risk analysis in underground tunnel construction with tunnel boring machines using the Best–Worst method and data envelopment analysis

BackgroundThe use of tunnel boring machines is continuously increasing; however, this activity faces many risks, resulting in negative events. MethodsA novel approach using the best–worst method and a data envelopment analysis model was proposed to analyze the risks of underground tunnel construction with tunnel boring machines. The proposed approach was validated using a realistic case study of metro construction in Thailand. ResultsThe proposed approach efficiently analyzed the risks and produced more solid findings. The most critical and least affected risks can be identified according to risk scores in descending order. ImplicationThis study contributes a new method of risk analysis that benefits project managers and stakeholders who design risk management plans to reduce the occurrence and mitigate the severity of metro works. OriginalityThe new risk analysis can obtain the best compromise ranking of risks based on decision-makers’ preferences, probabilities, and various consequences under different circumstances.

Evaluation and ranking of manufactured cars in Iran using Network Data Envelopment Analysis model

Evaluation and ranking of manufactured cars in Iran using Network Data Envelopment Analysis model

Measuring an efficiency aggregation of medical diagnostic laboratories: A window NDEA approach

The efficiency evaluation of laboratories, as one of the most significant areas of healthcare, plays a key role in the quality of laboratory management. The classic data envelopment analysis (DEA) models have overlooked intermediate products, internal interactions and dealt with analyzing the network within the “Black Box” mode. This results in the loss of important information, and at times, a considerable modification occurs in efficiency results. This article evaluated the efficiency of some selected medical diagnostic laboratories in the city of Tehran according to the network data envelopment analysis (NDEA) approach. We considered a four-stage structure with additional inputs and undesirable outputs. We obtain the labs’ performance over a period of 6 months in 2022 by the NDEA window analysis process. To this aim, a four-stage structure model of three chief medical diagnostic laboratory processes as the pre-test, the test, and the post-test is designed. We considered sustainability criteria (economic, social, and environmental) to appraise the performance of laboratories, thus helping to improve the social, economic, and environmental problems of medical diagnostic laboratories. By using the Delphi viewpoint, the criteria for efficiency evaluation are achieved. The results showed that laboratory unit No. 22 maintained the highest average overall efficiency, since the high accuracy of this unit’s laboratory results had led to many physicians recommending this unit to their patients. We found that the only laboratory unit No. 20 had a decreasing trend, as it is located in an area that abounds with administrative and educational centers. At the beginning of the exam period, then the summer holidays, and finally the wave of end-of-summer trips, a decline occurs in efficiency over the period of six months.

Two-Stage Data Envelopment Analysis Models with Negative System Outputs for the Efficiency Evaluation of Government Financial Policies

The main purpose of this study is to provide a comparative analysis of several possible approaches to applying data envelopment analysis (DEA) in the case where some decision making units (DMUs) in the original sample have negative system outputs. In comparison to the traditional model of Charnes, Cooper, and Rhodes (CCR) and the CCR model with a scale shift to measure second-stage outputs, the range directional measure (RDM) model produces the most appropriate results. In this paper, an approach is proposed for estimating returns to scale. The study applies a two-stage DEA model with negative second-stage outputs to assess the public support for research, development, and demonstration projects in the energy sector in 23 countries over the period from 2010 to 2018. The assessment of government performance depends on its contribution to the growth of energy efficiency in the national economy and the reduction of its carbon intensity. Intermediate outputs (patents in the energy sector) are included in the analysis as both outputs of the first stage and inputs of the second stage. Taking the similarity between the calculations obtained without stage separation and the system efficiency calculations from the two-stage model as a measure of model adequacy, the RDM model shows the highest similarity scores.

An Improved Inverse DEA for Assessing Economic Growth and Environmental Sustainability in OPEC Member Nations

Economic growth is essential for nations endowed with natural resources as it reflects how well those resources are utilized in an efficient and sustainable way. For instance, OPEC member nations, which hold a large proportion of the world’s oil and gas reserves, may require a frequent evaluation of economic growth patterns to ensure that the natural resources are best used. For this purpose, this study proposes an inverse data envelopment analysis model for assessing the optimal increase in input resources required for economic growth among OPEC member nations. In this context, economic growth is reflected in the GDP per capita, taking into account possible environmental degradation. Such a model is applied to the selected OPEC member nations, which suggests that in terms of increasing the GDP per capita, only one member was able to achieve the best efficiency (i.e., reaching the efficiency frontier), resulting in a hierarchy or dominance within the sample countries. The analysis results further identify the economic growth potential for each member country. For the case of Indonesia, the analysis suggests that further economic growth may be achieved for Indonesia without additional input resources. This calls for diversification of the nation’s economy or investment in other input resources. In addition, the overall results indicated that each member nation could increase its GDP per capita while experiencing minimal environmental degradation. Our analysis not only benchmarks the growth efficiency of countries, but also identifies opportunities for more efficient and sustainable growth.

Impact of environmental regulation intensity on the efficiency of sustainable economic growth in the European Union

Environmental pollution is an essential constraint to the sustainable growth of regional economy. This paper uses the super efficiency slacks-based measure data envelopment analysis (SBM-DEA) model to measure the efficiency of sustainable economic growth (ESEG) in the European Union (EU) and applies the spatial econometric model to explore the impact of environmental regulation intensity (ERI) on ESEG in the EU. The results show that the ESEG in the EU shows an N-shaped growth trend and the spatial pattern of “Western Europe > Eastern Europe > Central Europe > Northern Europe > Southern Europe” from 2001 to 2019. The impact of ERI on the ESEG in the EU presents a “U" shape, indicating that increasing ERI first reduces ESGE, and after reaching the turning point of ESGE, continuing to increase ERI will promote ESGE. Further analysis shows the ERI have spatial spillover effect on the ESEG, while the spatial spillover effect in the eastern and western regions of the EU is different. Improving the ERI in the western region is not only conducive to the ESEG of the country but also has a positive spillover effect on neighboring countries. However, the spatial agglomeration effect in the eastern region is not significant. In addition to environmental regulation, labor & capital structure, research and development (R&D) expenditure, GDP per capita and trade value have a significant impact on the ESGE of a country and neighboring countries.

Supply Chain Resilience: A Common Weights Efficiency Analysis with Non-discretionary and Non-controllable Inputs

Supply chain resilience (SCRes) as the supply chain network's (SCN) capacity is essential to recover from disruptions. The economic, environmental, and geopolitical regional characteristics of the Pacific region present many challenges and opportunities for building supply chain resilience. This study aims to measure the resilience of supply chains (SCs) considering the characteristics of the network under which they operate. In this study, we proposed a new common set of weights (CSW) model in data envelopment analysis to evaluate the resilience of SCNs. Many external variables beyond decision-makers’ direct control impact SC operations and their resilience. Therefore, the proposed CSW model formulates the non-discretionary and non-controllable inputs in measuring the resilience of SCNs and provides a complete ranking with a higher discrimination power. To improve SCRes, SC managers are recommended to enhance the clustering coefficient and node degree of their SCN by establishing more connections with other SCNs in order to pinpoint the essential capabilities that companies should prioritise in order to develop a stronger and more adaptable SC in the post-COVID-19 pandemic.

Research on the Operational Efficiency of Basic Medical Insurance for Urban Employees in China-Based on Three-Stage DEA Model.

To conduct a three-stage data envelopment analysis (DEA) of the operational efficiency of urban employee basic medical insurance (UEBMI) across 31 provinces in China from 2012 to 2021. Utilized a three-stage DEA model, this study measured and evaluated the operational efficiency of urban employee basic medical insurance within China's 31 provinces. The operational efficiency of urban employee basic medical insurance in China was notably low, displaying significant disparities across different regions and periods. Substantial room for improvement exists. Environmental factors, including urbanization level and unemployment rate, wielded a pronounced influence on the operational efficiency of China's employee medical insurance. Conversely, the fiscal revenue and expenditure ratio hindered the enhancement of employee medical insurance efficiency. This study held valuable insights for enhancing the operational efficiency of China's urban employee basic medical insurance. To effectively improve insurance efficiency, the following recommendations are put forth: firstly, relevant departments should escalate resource investments and optimize resource utilization based on rational allocation; secondly, enhance legislation and regulations, fortify fund oversight, and ensure equitable and judicious utilization of the medical insurance fund; finally, leveraging modern high-tech advancements can comprehensively elevate the operational efficiency of the employee medical insurance fund. Ultimately, with the advent of cutting-edge technology, the operational efficiency of employee medical insurance fund can be comprehensively elevated.

A Two-Stage Approach of DEA and AHP in Selecting Optimal Wind Power Plants

Sustainable development has become a global tendency in the growth orientation of governments. Renewable energies such as wind and solar have attracted many concerns in reducing the level of fossil fuels dependence. One of the most significant stages in such a transmission process is determining the most suitable location. This article aims to propose a novelty approach combining two multiple criteria decision making models, namely data envelopment analysis (DEA) and analysis hierarchy process (AHP) to recommend the most optimal site for wind power plants. Vietnam has been one of the most successful ASEAN nations in attracting renewable energy investments; hence, the article considers it as a case study. In the first stage, there are five inputs and outputs conducted by DEA models to choose the five most potential alternatives among 12 locations for the next stage. In the second stage, the AHP model is implemented on five main criteria and 20 subcriteria that are referred to the options of experienced experts. The findings show that Ninh Thuan province, which is obtained with the highest final score (0.374), is the most suitable location for wind power plants construction. The article has contributed to renewable literature in terms of methodologies as well as to practice for both public and private stakeholders.

Decoupling for Carbon Neutrality: An Industrial Structure Perspective from Qinghai, China over 1990–2021

Carbon neutrality is urgent as rapidly emerging economies aggravate their share of global energy demand. In China, the energy structure is dominated by fossil fuels, but it varies significantly across provinces. As an indicator of carbon neutrality, previous studies of decoupling between carbon dioxide emissions and economic growth focused at the national and sector levels in China. However, they overlook the role of industrial structure in decoupling at the provincial level. In this light, the following paper focuses on Qinghai Province, analyzing decoupling and its influencing factors for achieving carbon neutrality from an industrial structure perspective over 1990–2021. It uses the Tapio decoupling model to evaluate decoupling states and the Logarithmic Mean Divisia Index decomposition to evaluate the influencing factors. A Data Envelopment Analysis model of super-efficiency Slacks-Based Measure is used to evaluate the decarbonization efficiency. The study finds that the overall trend shifted from weak to strong decoupling. Strong decoupling dominated the primary industry while weak decoupling dominated the secondary and tertiary industries. Economic growth negatively impacted overall decoupling, while population had a marginal effect. Energy structure and intensity generally promoted decoupling. Additionally, the overall mean efficiency of decarbonization was 0.95, led by the tertiary industry. The paper concludes by discussing policy implications.

PENGARUH EKSPOR, IMPOR TEH DAN KURS TERHADAP CADANGAN DEVISA INDONESIA

This study aims to determine whether the company's condition is efficient through the efficiency analysis test. The method used in this analysis test is non-parametric quantitative with a Data Envelope Analysis (DEA) model. The data source is based on the financial statements of agricultural companies, where the input variables include Total Assets, Liabilities, and Land, while the output variable is Sales. Processed using MaxDea software. There are 12 sample companies or DMU in this study. The results of DMU analysis using VRS output orientation assumptions show that agricultural companies on an efficiency scale of 1 are 4.

ANALISIS EFISIENSI PERUSAHAAN PERTANIAN DI INDONESIA DENGAN METODE DATA ENVELOPMENT ANALYSIS (DEA)

This study aims to determine the condition of the company, efficient or not through the efficiency analysis test. The method in this analysis test is non-parametric quantitative with Data Envelopment Analysis (DEA) model. The data source is based on the financial statements of agricultural companies, where the input variables include; Total Assets, Liabilities and Land, while the output variable is Sales. Processed using MaxDea software. There are 12 sample companies or DMU in this study. The results of DMU analysis using VRS output orientation assumptions show that agricultural companies that are on an efficiency scale of 1 are 4 companies.

Efficient water use and reusing processes across Spanish regions: A circular data envelopment analysis with undesirable inputs

Efficiency is a fundamental element of the transition towards a sustainable use of natural resources among which water has a prominent role. Spain is among the European countries facing considerable water shortage problems, particularly within its southern regions. The current paper introduces an efficiency evaluation model based on Data Envelopment Analysis (DEA). The model incorporates a subset of outputs as undesirable inputs to account for the circularity of the production process by means of treated and reused water. The main differences existing between the circular approach proposed, a standard two-stage DEA model and a dynamic Slacks-Based Measure process will be carefully illustrated. In particular, the drawbacks faced by these latter techniques to consistently incorporate circularity into their optimization structures will be demonstrated both formally and numerically. The malleability of the current approach and its capacity to generate consistent identifiable categories among the regions evaluated within a circular production setting will be studied in detail. The analysis performed demonstrates significant structural differences in production capacity and efficiency across regions, with the inefficient ones displaying a considerable subpar use of resources through their production processes. The prevalence of inefficiency in several northern regions is partially compensated through their treatment and reutilization of water. However, this is not the case for the southern ones, which face stricter constraints and more inefficient production processes. The subsequent implications for efficiency are examined and policies aimed at enhancing the structural quality of the corresponding production systems suggested.

Role of mineral-based industrialization in promoting economic growth: Implications for achieving environmental sustainability and social equity

This research's problem statement centers on balancing economic development with environmental protection and social well-being. As mineral resources represent a crucial driver of economic growth in these regions, examining how their exploitation impacts economic indicators and the broader dimensions of sustainability and equity is imperative. Over the past decade (2010–2020), the role of mineral-based industrialization in fostering economic growth has garnered significant attention, particularly in Africa, Asia, and Latin America. This study delves into the complex interplay between mineral-based industrialization, environmental sustainability, and social equity in these regions, employing the Data Envelopment Analysis (DEA) model as a critical analytical tool. The results of this study reveal multifaceted insights. While mineral-based industrialization has indeed contributed to economic growth in these regions, the environmental implications are significant. Often, the extraction and processing of minerals come at the cost of environmental degradation, posing challenges to sustainability. Moreover, the distribution of benefits from this growth is often unequal, exacerbating social inequities. This research underscores the complex dynamics surrounding mineral-based industrialization in Africa, Asia, and Latin America. It highlights the need for a nuanced approach considering economic growth, environmental sustainability, and social equity. Policymakers and stakeholders must carefully navigate these challenges to ensure that mineral resources serve as catalysts for comprehensive and inclusive development in the future.

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artificial neural networks (ANNs), support vector machine/regression (SVM/SVR), projection pursuit regression (PPR), etc., have been viewed as beneficial for managers in predicting system behaviors. PPR is especially suitable for small and non-normal distribution samples, the usual cases in DEA analysis. This paper integrates DEA and PPR to cover the shortcomings we faced while using DEA and DEA-BPNN, DEA-SVR, etc. This study explores the advantages of combining these complementary methods into an integrated performance measurement and prediction model. Firstly, the DEA approach is used to evaluate and rank the efficiency of DMUs. Secondly, we establish two DEA-PPR combined models to describe the DEA efficiency scores (also called the production function) and the DEA-efficient frontier function. The first combined model’s input variables are input–output indicators in the DEA model, and the output variable is the DEA efficiency. In the second model, its input variables are input or output indicators in the DEA model, and the output variable is the optimal input indicator for input-oriented DEA or the output indicator for output-oriented DEA. We conducted positive research on two examples with actual data and virtual small, medium-sized, and large samples. Compared with the DEA-BPNN and DEA-SVR models, the results show that the DEA-PPR combined model has more vital global optimization ability, better convergence, higher accuracy, and a simple topology. The DEA-PPR model can obtain robust results for both small and large cases. The DEA-BPNN and DEA-SVR models cannot obtain robust results for small and medium-sized samples due to overfitting. For large samples, the DEA-PPR model outperforms DEA-BPNN, DEA-SVR, etc. The DEA-PPR combined model possesses better suitability, applicability, and reliability than the DEA-BPNN model, the DEA-SVR model, etc.

How to Comprehensively Evaluate Firm Performance from Operational, Financial, and Sustainability Perspectives? A Two-Stage Data Envelopment Analysis Approach

ABSTRACT This study proposes a two-stage modeling framework to evaluate the efficiency of firm performance based on sustainability (environmental, social, and governance (ESG)), operational and financial measures. Diverging from previous studies, this study conceptualizes firm performance as two serially linked stages, with operational performance as the first stage and market performance as the second stage. Acknowledging that ESG activities may reflect firm value and that these activities depend on a firm’s operational performance, ESG scores are modeled simultaneously as an output of the operational performance stage and as an input for the market stage. Under this performance appraisal framework, two-stage data envelopment analysis models capturing overall and stage-level efficiency are constructed. The application of the proposed modeling framework is demonstrated using a sample of large Saudi listed firms as a case study of an emerging market. The main conclusion is that ESG legislation, regulations, and disclosure guidelines should be well defined in emerging markets to increase the overall efficiency of listed firms’ performance.