Data Envelopment Analysis Model Research Articles

Based on the New Dynamic Network DEA and Malmquist Index of High-Quality Innovation in the New Energy Vehicle Economy Development Research of the Internet of Things

This paper takes the enterprise economic data of new energy vehicles (NEVs) in 2018–2023 as the research background, combined with the performance evaluation theory of the NEV industry, and systematically discusses how to make the high-quality innovative development of NEVs as the research purpose. We use the new dynamic network data envelope analysis (DEA) model, Marmquist Index and other and the Internet of Things internationally recognized, scientific and effective data models as research methods to make an in-depth analysis of the business performance of five representative enterprises on multi-quantitative input-output issues. The research results show that the NEV industry has a good development prospect and economic foundation on the whole, but it still faces challenges such as limited space for core technology progress, insufficient carbon emission reduction and environmental protection planning. At the end, this paper puts forward innovation suggestions such as accelerating technological innovation, optimizing production process and responding to preferential policies and exploring international market.

Assessing the Impact of China’s Industry on Health: A Dynamic Three-Stage Bootstrap Model with an Endogenous Fuzzy Directional Distance Function

Evaluating the industrial eco-system is essential for promoting resource efficiency, environmental development, and public health protection. However, traditional Data Envelopment Analysis (DEA) methods are prone to biases and rely on precise numerical values, which pose significant challenges when evaluating the industrial ecosystem in China, particularly given the prevalent data uncertainties and environmental complexities. Additionally, DEA models are typically static, fail to capture the long-term trends and dynamic characteristics of the industrial ecosystem. To address these issues, this study proposes a dynamic three-stage model with an endogenous fuzzy directional distance function. The model integrates the generalized smooth bootstrap method and fuzzy comparison techniques to correct errors and account for data uncertainties, improving the accuracy and scientific validity of eco-efficiency assessments. Chinese enterprises face challenges such as excessive resource consumption, environmental pollution, and health risks, necessitating a more comprehensive and flexible evaluation system to adapt to the complex and dynamic nature of the industrial ecosystem. By focusing on industrial production, environmental governance, and health threats (IPEGHT) in China, the research aims to provide a robust framework for enhancing industrial eco-efficiency in a dynamic and uncertain environment. The results show: (1) The proposed method for selecting endogenous direction vectors is applied to the industrial production stage, thereby identifying the subsequent improvement directions for industrial production in various provinces. (2) The idea of integration is adopted to introduce the generalized smooth bootstrap method and fuzzy mathematics, to conduct a precise and comprehensive evaluation of the IPEGHT industrial eco-system. (3) From 2011 to 2021, China’s IPEGHT industrial eco-system efficiency showed a step-like distribution, and attention needs to be paid to environmental governance. The proposed method’ application can facilitate the integration of DEA and fuzzy mathematics, and fosters a more sustainable industrial eco-system.

Enhancing Firm Efficiency Measurement Using DEA and Fuzzy Approaches with Integrated Corporate Social Responsibility

Purpose: We propose a new scientific approach by integrating Corporate Social Responsibility (CSR) into the measurement of firm efficiency through a dynamic fuzzy Data Envelopment Analysis (DEA) model. It aims to investigate the relationship between CSR practices and firm performance over time across industries.Findings: Higher CSR engagement companies appear to obtain better efficiency scores and show resilience when adapting to external challenges. Especially, capital-intensive industries where environmental risk is high benefit most from integrating CSR into their operational structures. The corresponding dynamic fuzzy DEA model introduced by Yang, et al. (2023) successfully analyses the non-neutrality of input metrics on CSR resilience, along with uncertainties inherent in CSR metrics, hence offering valuable insights into their industry-specific and temporal variability.Novelty: This research is the first to apply a dynamic fuzzy DEA approach to integrating CSR in terms of a key measure of firm efficiency. This study fills the gap between sustainability practices and operational performance by addressing the complexity of multifaceted impacts of CSR.Implications: These results provide practical recommendations for policy makers and managers to reflect the reconciliation of environmental or social objectives with financial performance in CSR strategies. Moreover, the methodological framework paves the way for other studies to consider dynamic and uncertain variables into efficiency measurement.

Spatial-temporal characteristics and drivers of urban built-up areas land low-carbon efficiency in China

Understanding the evolution of low-carbon efficiency in urban built-up areas is essential for developing countries striving to meet sustainable development goals. However, the mechanisms driving low-carbon efficiency and the associated development pathways remain underexplored. This study applies the Global Data Envelopment Analysis (DEA) model, the Global Malmquist-Luenberger Index, and econometric models to evaluate low-carbon efficiency and its determinants across China’s urban built-up areas from 2010 to 2022. The findings reveal a significant increase in efficiency, from 0.555 in 2010 to 0.785 in 2022, reflecting an overall improvement of 41.4% (P < 0.05). Spatially, low-carbon efficiency demonstrates a pronounced “east-high and west-low” distribution, highlighting regional disparities and spatial correlations. Temporal changes in low-carbon efficiency are primarily driven by technological advancements and shifts in the technological frontier. However, the disproportionate efficiency gains during periods of high resource input suggest inefficiencies in production factor allocation, particularly in densely populated urban centers. Unlike natural endowments, concentrated factor inputs in such cities often impede efficiency improvements. The findings underscore the need for context-sensitive development strategies, as a one-size-fits-all model may not address the diverse challenges posed by regional disparities. By leveraging market mechanisms to optimize resource allocation and strengthen interregional spatial connectivity, this study provides actionable insights for promoting sustainable land development in developing economies.

A novel DEA-Tobit-SD assessment framework and application of provincial-level carbon emission embracing regional heterogeneity

Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces’ key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed.

Comprehensive Analysis and Emission Reduction Pathway of GHG Emissions at a Wastewater Treatment Plant in Suzhou

Taking a sewage treatment plant in Suzhou City, Jiangsu Province, as an example, the greenhouse gas （GHG） emissions generated in the sewage treatment system were calculated using the carbon balance method and the emission factor method. The environmental impacts and economic aspects of different treatment units in wastewater treatment plants were analyzed using life cycle assessment, cost-benefit analysis, and data envelopment analysis models, and emission reduction pathways were proposed. The results indicated that the total GHG emissions （in terms of CO2） from a certain municipal wastewater treatment plant in Suzhou were 6 653.08 kg·（104 m3）-1, with direct and indirect GHG emissions accounting for 29.22% and 74%, respectively. The reuse of treated effluent achieved a reduction of 3.3% in emissions. The biological treatment phase and the sludge treatment phase were the main impact stages for GHG emissions at a certain wastewater treatment plant in Suzhou, where the high-power equipment, specifically the blowers used in the biological treatment phase, and the use of polymeric ferric sulfate agents in the sludge treatment phase were the primary factors contributing to GHG emissions. Life cycle assessment analysis revealed that electricity consumption, direct CO2 emissions, pollutant concentration in the effluent, and the use of chemical agents at wastewater treatment plants had negative impacts on global warming, atmospheric acidification, and eutrophication of water bodies. Calculations indicated that for every 10 000 m3 of wastewater treated, the sewage treatment plant achieved a net benefit of 13 630 RMB. However, from April to May 2023, the scale efficiency of the sewage treatment plant was less than 1. This indicates that during this period, the proportion of output increase was less than that of input increase, demonstrating an irrational structure of input-output. After June, through enhancing the overall operational load, advancing technical improvements, and management efforts, the optimization of scale efficiency was achieved. A sewage treatment plant in Suzhou could achieve the goal of being "green and low-carbon" by installing high-efficiency pumps and fans, utilizing solar photovoltaic and water source heat pump systems, and making process improvements.

Performance Evaluation of University-Based Scientific Research Institutions With a Non-Independent Parallel System.

Performance management in university-based scientific research institutions is essential for driving reform, advancing education quality, and fostering innovation. However, current performance evaluation models often focus solely on research indicators, neglecting the critical interdependence between the education and research systems. This oversight leads to inefficiencies in resource allocation and an underestimation of overall institutional performance, particularly in universities with varying development levels. To address these deficiencies, this study introduces two innovative two-stage data envelopment analysis models: the group-frontier and meta-frontier evaluation models. The findings are validated using data from 144 universities across China. They demonstrate that the proposed models effectively mitigate the underestimation of efficiency in traditional models and accurately reflect the intertwined nature of university subsystems and the disparities in university development. These results offer valuable insights for improving the performance of scientific research institutions and informing the strategic decisions of university administrators and government education departments.

Measuring the Effects of Sediment Variation and Evaporation Loss on the Overall Operational Efficiency of Hydropower Systems: A Robust Network Data Envelopment Analysis Model

Measuring the Effects of Sediment Variation and Evaporation Loss on the Overall Operational Efficiency of Hydropower Systems: A Robust Network Data Envelopment Analysis Model

A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems

A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems

Demystifying the efficiency in managing the working capital through the lens of sustainability

PurposeThis study aims to scrutinize the role of environmental, social and governance (ESG) performance and its indicators in achieving working capital management (WCM) efficiency.Design/methodology/approachUsing a sample comprising 520 firm-year observations from Indian listed firms for the period from 2017 to 2021, the current study estimates WCM efficiency using the Banker, Charnes and Cooper (BCC) input-oriented model of data envelopment analysis (DEA). In addition, this study performs baseline, robustness and heterogeneity tests to examine the effect of ESG performance and its components on WCM efficiency.FindingsOur findings show that firms with high ESG performance better manage short-term liquidity. Also, environmental performance (EP), social performance (SP) and governance performance (GP) highlight a similar positive association with WCM efficiency. As per the heterogeneity test results, both high- and low-sustainable firms showcase the necessity of ESG performance to achieve efficiency in managing their working capital.Practical implicationsThe findings emphasize the need for managers and policymakers to integrate sustainable practices with financial strategies, enhancing both short-term stability and long-term sustainability goals and thereby guiding effective policy and governance enhancements.Originality/valueWe attempt to adjudicate the role of sustainability on WCM efficiency from an emerging country perspective, which has not yet been explored. Our study also makes a methodological contribution by pioneering the DEA in the context of ESG and working capital.

An enhanced hybrid interval slacks-based DEA model with super-efficiency and application

Data envelopment analysis (DEA) models play a significant role in evaluating the efficiency of decision-making units (DMUs) among multiple input and output variables in many fields, such as industry or economy. This method establishes a production possibility set (PPS) that defines the efficient ones, the efficiency boundary or frontier, and provides the projection into the latter, or targets, for the inefficient ones. This paper introduces an enhanced hybrid interval slacks-based DEA when information on inputs and outputs can be discrete (integer) or continuous (real) and presents uncertainty, which can be modeled using intervals (integer or continuous). To this matter, we propose a novel approach that calculates crisp inefficiency scores, interval slacks, and interval targets using interval arithmetic and LU-partial orders on intervals. Mainly, the novelty is based on a reduction and simplicity of the model with respect to others existing, as well as the inclusion of integer interval-valued information with the corresponding mathematical tools and previous results. A super-efficiency slacks-based model is also provided to distinguish among the efficient DMUs and provide a full rank for the set of DMUs. We finally present the practical application of our method in tourism, an essential economic sector in Spain, focusing on different coastal areas.

Operational Efficiency of Public Hospitals in Greece During the COVID-19 Pandemic: A Comparative Analysis Using DEA and AHP Models.

This study evaluates the efficiency of public hospitals in Greece during the COVID-19 epidemic in 2020, using Data Envelopment Analysis (DEA) and the Analytical Hierarchy Process (AHP). Faced with unprecedented pressure from increased demand for medical services, these hospitals had to adapt quickly while playing a crucial role in supporting local economies, similar to the effect of tourism on rural economies. This study reveals that, despite average efficiency scores of 83% for result-oriented models (BCC) and 65% for constant return models (CCR), inefficiencies of scale emerged under the pressures of the pandemic. The AHP, by incorporating qualitative criteria and decision-makers' preferences, offers a valuable perspective but shows little correlation with DEA's quantitative results. This research emphasizes the importance of utilizing integrated methods to formulate a more comprehensive assessment, adapted to the complex challenges of the healthcare sector during crisis periods.

Assessing cross-docking performance using a novel network DEA model regarding sustainability and undesirable factors under uncertainty

Although evaluating warehouses' efficiency using different approaches has been gaining prestige in recent years, this study is the first attempt to assess the efficiency of cross-docking systems. For this purpose, a comprehensive structure of a cross-docking system is examined, where a broad range of components affecting its performance are considered, including inbound and outbound doors, various means of transportation, inspection, kitting, storage, retrieval, and staging operations. In addition, an extensive set of key performance indicators (KPIs) are introduced for each component regarding automation, digitization, resiliency, sustainability, and lean concerns. More importantly, a novel network data envelopment analysis model is proposed to evaluate the efficiency of the cross-docking system with respect to undesirable factors. Furthermore, a new hybrid uncertainty approach is offered to handle the uncertainties in introduced KPIs and quantify some qualitative judgments. Finally, a case study is examined to illustrate the effectiveness of the proposed model and uncertainty approach. The obtained results reveal that the simultaneous exploitation of pre-distribution and post-distribution policies can increase the efficiency of the examined system by 36%. Also, modifying the layout to eliminate redundant transportation operations can lead to an 18% increase in efficiency.

Measuring Relative Efficiency of Life Insurance Companies with Data Envelopment and Fuzzy Data Envelopment Analysis

Insurance sector in India is one of the flourishing sectors of the economy. The insurance sector plays a vital role as it minimizes the risk and mobilizes the resource. Studying the efficiency of the insurance companies is highly important because it helps in determining how insurance companies respond to the various challenges and how many of them are likely to survive those challenges in the event of their occurrence. So, it becomes necessary to analyse the performance of life insurance companies in India with the application of Data Envelopment Analysis (DEA) and its extension Fuzzy Data Envelopment Analysis (FDEA). The empirical results of the Conventional DEA models and Fuzzy DEA models are computed to trace the performance of decision making unit at a possibility level. The computed fuzzy efficiency scores need to be ranked in order to determine how each DMU performs relative to the other DMUs in an uncertain environment. Chen and Klein (1997) ranking method were applied to rank the fuzzy numbers. To compare the two ranking methods the concept of Spearman rank correlation is applied. From this study the author infer that fuzzy DEA is more efficient than conventional DEA (CRS and VRS) Approach.

Assessing Social Sustainability Efficiency in European Countries: Focusing on Health and Well‐Being

ABSTRACTThis study investigates the challenges of achieving social sustainability efficiency in European countries, especially in health and well‐being, under sustainable development goal 3 amid demographic aging pressures. Using an innovative modified circular dynamic three‐stage systematic network data envelopment analysis model, this research evaluates the influence of social protection investments on healthcare and well‐being systems. The findings reveal that Denmark, Estonia, France, and Germany demonstrate optimal efficiency, while Lithuania, Luxembourg, the Netherlands, and Slovenia require strategic improvements, particularly in healthcare efficiency. Policy recommendations underscore the need for enhanced healthcare technology, occupational safety, and adaptive strategies to support social sustainability across 24 countries from 2016 to 2020. This approach offers a comprehensive efficiency framework and actionable insights to guide future policy development to address European healthcare efficiency gaps.

Dynamic evaluation and sensitivity analysis of China’s industrial solid waste management efficiency based on ecological environment cycle perspective

IntroductionThe recycling and repurposing of industrial solid waste is a crucial element of sustainable development in growing industrialized nations.MethodsAn advanced two-stage Data Envelopment Analysis (DEA) model was employed to assess the effectiveness of solid waste management across all 31 provinces in China throughout the period spanning from 2016 to 2022.ResultsThe research findings suggest the following: (1) The circular economy has shown varying degrees of improvement in efficiency across most regions. (2) The average efficiency of the resource reuse stage is not higher than 0.4, indicating significant room for development that requires serious attention. (3) The western region has a higher average solid waste treatment efficiency of 0.65 compared to 0.53 in the eastern region, while the central region falls at a moderate level. Furthermore, this work employed sensitivity analysis to examine the resilience of regional efficiency research and discovered that the overall findings remain statistically significant. Hence, it is imperative to simultaneously enhance the internal administration of businesses and enhance governmental environmental legislation and regulations, with the aim of attaining the utmost optimization of resource recycling.DiscussionThis paper presents policy suggestions for enhancing the solid waste recycling system within provincial government departments, while also establishing the foundation for the expansion of the solid waste treatment industry, which is necessary to accomplish the goal of “double carbon”.

How can the natural background and ecological & environment promote the green and sustainable development of Chinese tourist attractions?

In the context of the global carbon peak and carbon neutrality initiatives and post-pandemic, studying the green and sustainable development of tourist attractions is of great significance for the sustainable utilization of tourism resources. This study focuses on tourist attractions in 30 provinces in China from 2001 to 2019, establishes an input–output indicator system for economic efficiency and eco-efficiency, and uses the Super-SBM model in Data Envelopment Analysis to calculate the economic efficiency and eco-efficiency of tourist attractions in China. To analyze the natural background and environmental driving factors that affect eco-efficiency, as well as the interaction between these factors, using a geographic detector model, and propose a green and sustainable development path for tourist attractions. The research results indicate that the eco-efficiency of Chinese tourist attractions was higher than the economic efficiency, and both showed a downward trend. The proportion of altitude and nature reserve area to the area under the jurisdiction, as well as the total investment in environmental pollution control, have a significant impact on eco-efficiency; The interaction between temperature, precipitation, and normalized difference vegetation index (NDVI), and the proportion of nature reserves in the jurisdiction and the total investment in environmental pollution control, is significantly enhanced, indirectly affecting the eco-efficiency of Chinese tourist attractions. Among the natural factors, temperature, precipitation, and NDVI all could interact with altitude to significantly the impacts on the eco-efficiency of Chinese tourist attractions. The research aims to provide a Chinese solution for developing tourist attractions in developing countries similar to China.

Efficiency improvement and the carbon-reduction effects of transport restructuring in China

Abstract The growth rate of transportation sector’s carbon dioxide (CO2) emissions is rigid, making it difficult to reduce emissions, especially for developing countries. The existing literature has not yet fully investigated the CO2 emissions reduction effect of transportation structural adjustment from the perspective of improving productivity, which is defined by total factor energy efficiency (TFEE) in this study. To close this knowledge gap, we first evaluated the TFEE of the transportation sector as a whole and its sub-sectors in 30 provinces in China from 2013 to 2021 based on the parallel data envelopment analysis (DEA) model and then estimated the energy conservation and emissions reduction potentials brought about by the optimization of the transportation structure based on the Long-range Energy Alternatives Planning (LEAP) model. The main results show: (1) TFEEs vary greatly across sub-sectors and provinces, with provinces performing differently across sub-sectors. (2) Compared with the business-as-usual (BAU) scenario, the structure enhancement (SE) scenario of structural adjustment can cumulatively reduce CO2 emissions by 289.20 Mt by 2030. (3) In the SE scenario, the energy demand will peak in 2026 and the CO2 emissions will peak at 1509.55 MT in 2027. These results indicate that the CO2 reduction from the transportation restructuring (structural effect) can exceed the increase in CO2 emissions following an increase in transportation activity (scale effect) and eventually bring down the total CO2 emissions for the transportation sector, despite the TFEE rankings of the subsector varying across regions. Therefore, policies that value regions with large volumes, a high share of road transport, and well-established infrastructure, can achieve greater potential to reduce carbon emissions.

Efficiency and Fairness in Financing Slovak National Sports Associations

Abstract National sports associations are private-sector sports organisations with an exclusive, monopoly position for a particular type of sport within individual countries. This monopoly position is done by the internal regulations of international sports associations, of which only one entity for a given country can become a member, which its country represents at the international level. The legislation of individual countries also regulates National sports associations. The main aim of this contribution is to assess and benchmark the technical efficiency of 70 sports associations offering their services in Slovakia by a set of Data Envelopment Analysis (DEA) models and to consider fairness in the financing of Slovak national sports associations. It is important to note that all models used have different premises in their modelling, each of them solves optimisation problems of real live under specific conditions. The arguments for evaluating fairness in financing are based on the methodology of the breakdown of financial subsidies for sport and on controlled interviews with experts of the state administration and national sports associations. Based on our analysis, we can state that the system of financing Slovak national sports associations is neither efficient nor fair in the redistribution of public funds from the point of view of meeting the goals of public policy in the field of sport. The outcome of the research may be of interest to regulatory institutions, as well as to the management of individual national sports associations.

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.