Assurance Region Research Articles

Regional carbon emission efficiency evaluation combining gray forecasting and game cross-efficiency analysis: The case of Yangtze River Delta

Carbon emission efficiency (CEE) reflects the interplay between carbon emissions and the economy, which refers to achieving more economic benefits and lower carbon emissions while considering energy, labor, and capital inputs. Assessing regional CEE is crucial for evaluating the level of China's regional low-carbon economic development. Thus, this paper proposes a scenario-based hybrid model with a foresight perspective and game cross-efficiency (GCE) analysis. It measures the future CEE of 41 Yangtze River Delta (YRD) cities from 2023 to 2030. The improved gray forecasting models generate the input and output datasets for GCE analysis, and the assurance region constraint simulates the carbon emission and energy consumption dual-control policy. The results show that: (1) the CEEs of 41 cities are generally low, with an average of 0.2142. Shanghai has the highest CEE, 0.8089, while Tongling has the lowest, 0.0307, under the current policy constraint. (2) Under the four control policy scenarios, the CEE of the YRD urban agglomeration generally follows a U-shaped trend. It indicates that the dual-control policy may lead to a short-term decline in the CEE of YRD, but in the long term, it may gradually increase in 2025 or 2026. (3) Spatial–temporal analysis reveals that the government should flexibly optimize and update the carbon intensity constraint value based on regional development differences and focus more on energy consumption. These results provide forward-looking guidance for China's regional low-carbon and high-quality development.

A Long-Term and Global Energy Security Performance Evaluation Model Integrating Principle Component Analysis, Assurance Region Approach, and the Malmquist Technology

A Long-Term and Global Energy Security Performance Evaluation Model Integrating Principle Component Analysis, Assurance Region Approach, and the Malmquist Technology

An exploration of operational efficiency, market efficiency, and sustainable development in the banking industry

AbstractSustainable development is the primary goal of the current global economic transformation. The banking industry, as an intermediary institution within the financial system, not only serves as a crucial foundation for national economic development but also plays a key role in the global transition to sustainability and a low‐carbon economy. Banks can leverage the influence of financing to direct funds towards sustainable ventures and simultaneously develop green financial products, guiding ordinary consumers to make more environmentally friendly choices. How the banking industry can effectively improve operational efficiency while considering sustainable development is one of the key focuses of ongoing development in the banking sector. The main aim of this study is to incorporate the aspect of sustainable development into the evaluation of operational efficiency, aligning with the goals of corporate sustainable development. It allows for an analysis of how decision‐makers in the banking industry can adjust resources to maintain stable and sustainable profitability while promoting sustainable development. This study selected 13 financial holding banks and 19 non‐financial holding banks in Taiwan as research subjects, covering the period from 2016 to 2021. Utilizing a meta assurance regions (AR) parallel dynamic two‐stage slacks‐based measure model with “operational stage” and “market and sustainability parallel stage,” the research explores the efficiency performance of banks considering sustainable development. It examines the differences in efficiency performance at various stages among different types of banks. The research findings are as follows: (1) among the banking industry in Taiwan, operational stage efficiency ranks the highest, followed by sustainable stage efficiency, while market stage performance lags behind. (2) Financial holding banks outperform non‐financial holding banks on average in operational stage, market stage, and sustainable stage efficiency. (3) Fubon Financial, TFH, CATHAY HOLDINGS, and SKFH are benchmark performers among financial holding banks. (4) Non‐financial holding banks exhibit a higher average technical gap ratio compared to financial holding banks, indicating that non‐financial holding banks have fewer inefficient areas to improve. However, the technical efficiency of both groups of banks approaches each other over the years. (5) In the sustainable stage, financial holding banks demonstrate superior environmental indicators and corporate treatment efficiency compared to non‐financial holding banks.

УДОСКОНАЛЕННЯ ІСНУЮЧИХ МЕТОДІВ ОЦІНЮВАННЯ ФУНКЦІОНУВАННЯ АЕРОПОРТІВ ПРИ МІЖНАРОДНІЙ ІНТЕГРАЦІЇ

The article is devoted to the study of methods for assessing the operation of airports under international integration and their improvement. The recent scientific studies on assessing the operation of international airports were analyzed and it was found that the most actively used methods are the initial data envelopment analysis (DEA) and various variants of quantile regression, Tobit regression, assurance region methods, SBI and DMU, Malmquist index, classification and regression tree, bootstrapping tool and other tools. It was confirmed that the existing methods for assessing the airports’ operation have a significant number of indicators for calculations based on the evaluation of economic, technical or overall efficiency. The parametric methods for assessing the operation of airports, their advantages and disadvantages were characterized. The methodology of stochastic frontier analysis (SFA), the use of a non-parametric approach to evaluate airport performance, and the evaluation of the overall efficiency of two-stage DMUs with shared resources were described. The methodical approaches based on the use of the spatial regression model, the AAG model and the stochastic frontier model were characterized. The methodology for determining the target function Effi to assess the efficiency of each airport DMUi was described. The scheme for assessing the airports’ operation by input and output indicators was proposed, which includes a three-module assessment system for the passenger, cargo and joint modules. The cargo module is the authors’ own development and its creation is justified by the fact that a significant number of air cargo flows are served by passenger aircraft. The group of input indicators of the cargo module includes the length of the loading and unloading work front, the number of cargo receiving points, the working area of rack and stack storage, the number of electric forklifts and lift trucks, as well as the number of cargo warehouses, and the number of general and special categories of cargo are defined as output indicators.

Non‐performance loans, operation, and recycle efficiency analysis—Dynamic Two‐stage Directional Distance Function Recycle with Assurance Regions model

AbstractPrevious studies have failed to analyze a bank's operation performance and non‐performing loans at the same time. Therefore, this research uses the Dynamic Two‐stage directional distance function (DDF) Recycle with Assurance Regions (ARs) model to explore the efficiency of Taiwanese banks in their operation stage and recycling stage. The research results are as follows. (1) The six‐year overall average efficiency is .7966. Taipei Star Bank has the best efficiency, and Fubon, Bank SinoPac, and Union Bank have the worst efficiency rankings. (2) Most banks have good efficiency in the operation stage, while efficiency in the recovery cycle stage fluctuates.

Synthetic Data Generation for Data Envelopment Analysis

The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole.

An alternative assessment approach to global logistics performance evaluation: Common weight H‐DEA approach

AbstractThe goal of this study is to investigate the objective importance of the categories and sub‐indicators constituting the Logistics Performance Index (LPI) and try to answer which of these LPI's indicators has the most significant importance and to provide a guidance tool for policymakers to devise policies in logistics and transportation. Hence, this study puts forth an integrated multi‐attribute decision‐making technique coalesced with the hierarchical data envelopment analysis and assurance region. For different income groups and regions, this model is employed to reassess the scores and rankings of LPI that are obtained from the World Bank. Displaying a higher objectivity than World Bank's method, the results demonstrate no substantial difference in the rankings obtained under both the proposed and World Bank's methods. Moreover, service delivery performance was found to be more vital to LPI than policy regulations. In terms of global weighting, of the six sub‐indicators, timeliness is the most important. These findings provide a guidance tool for policymakers to devise policies in logistics as well as in transportation. With the common weights that have been utilized, the results may direct toward different priorities in terms of policies, compared to World Bank.

Measuring the efficiency of administrative divisions in combating the COVID‐19 pandemic in Taiwan: an empirical study

AbstractThe coronavirus disease 2019 (COVID‐19) pandemic continues to have a substantial impact on the economies and livelihoods of all countries. Central and local government administrators are facing a critical issue of resource utilization efficiency due to their limited resources. This paper employs data envelopment analysis (DEA) and the concept of an assurance region (AR) to assess the relative efficiency of administrative divisions in Taiwan, whose effectiveness has been praised by other countries’ administrators and citizens, regarding COVID‐19 pandemic prevention and control. The input factors used were the cumulative number of deaths (an undesirable factor) and expenditures, and the output factors were the average number of unconfirmed citizens aged 65 and older, the average number of unconfirmed citizens aged 0–64, and the cumulative number of vaccinations. Notably, because there are many COVID‐19 variants with different characteristics, reaching a consensus AR among experts is difficult. Thus, different ARs were compared, and the most representative AR was identified for subsequent analysis. The main findings show that only Kaohsiung City was efficient, and Taipei City was the third worst due to inefficient operations. In addition, the administrative divisions were classified into four groups using cluster analysis based on the decomposition of aggregate efficiency to the contributions of the three outputs. The findings presented in this paper serve as a reference for both local government administrators and citizens, offering insights into relative efficiency and improvement directions. Moreover, they also provide guidance for resource allocation, future strategy adjustment, and development for central government administrators and citizens worldwide.

Analyzing the accuracy of variable returns to scale data envelopment analysis models

The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA model that delivers the most precise estimates. To address this issue, we advance the Monte Carlo simulation-based data generation process proposed by Kohl and Brunner (2020). The developed process generates an artificial dataset using the Translog production function (instead of the commonly used Cobb Douglas) to construct well-behaved scenarios under variable returns to scale (VRS). Using different VRS DEA models, we compute DEA efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed by a benchmark value and ranking as well as statistical hypothesis tests to evaluate the quality of the efficiency estimates. The procedure allows us to determine which parameters negatively or positively influence the quality of the DEA estimates. It also enables us to identify which DEA model performs the most efficiently over a wide range of scenarios. In contrast to the widely applied BCC (Banker-Charnes-Cooper) model, we find that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better. Thus, we endorse the use of AR and SBM models for DEA applications under the VRS regime.

Energy and environmental efficiency of China's regional electric power industry by considering renewable energy constraints

The renewable electricity quota standard (RES) policy plays an important role in achieving sustainable and green development targets. Thus, it is essential to measure the sustainability of the electric power sectors in the context of RES. In terms of the energy and environmental efficiency (EEE) measurements, few studies divided energy inputs into renewable and non-renewable energy or explored the restricted problem of minimizing resource summation. To fill these gaps, we propose an improved fix-sum energy input data envelopment analysis (FSIDEA) model based on the adjustment strategy of weighted sum minimization of renewable energy inputs. The assurance region (AR) restriction and renewable energy input sum constraint (IC) are, respectively, imposed on the FSIDEA model to achieve the common equilibrium effective frontier (EEF). Finally, this study assesses the EEE of the electric power sectors in China's 30 main regions based on the common EEF. The result reveals that Beijing and Shanghai can be selected as benchmarks after efficiency comparison. In addition, our proposed model can compressively rank all decision-making units (DMUs) compared with traditional efficiency models.

On VEA, production trade-offs and weights restrictions

In this article we explore the relationship between Value Efficiency Analysis (VEA) and Data Envelopment Analysis (DEA) models including production trade-offs or weights restrictions. In particular, we show that the VEA model is equivalent to a DEA model including production trade-offs for which the trade-off coefficient vectors are equal to either (i) the negative of the input and output quantities of the Decision Making Units (DMUs) chosen as the Most Preferred Solution (MPS) in VEA, under constant returns to scale, or (ii) the deviations of all evaluated DMUs’ input and output quantities from those of the DMUs chosen as the MPS, irrespectively of the returns-to-scale assumption. These trade-offs are the dual forms of type II Assurance Region weight restrictions. We then show that a similar equivalence holds between pure output or input VEA and DEA models including trade-offs, if the above trade-offs are respectively considered only for outputs or only for inputs. In this case the trade-offs are the dual forms of type I Assurance Region weight restrictions.

ANALİTİK AĞ SÜRECİ VE VERİ ZARFLAMA ANALİZİ İLE İŞLETMELERİN ETKİNLİK ÖLÇÜMLERİ ÜZERİNE BİR UYGULAMA

In this study, the financial efficiency of enterprises registered in the BIST Food, Beverage and Tobacco index was measured using Data Envelopment Analysis and Analytical Network Process Methods. Within the scope of the analysis, 18 enterprises were considered as decision-making units, and efficiency measurements were made on 6 input and 3 output variables. CCR, BCC and Assurance Region models of Data Envelopment Analysis were used in the application phase. Analytical Network Process method was used to obtain the weight constraints required by the Assurance Region model. As a result of the application, it was observed that the lower and upper limits used in the Assurance Region model decreased the number of decision making units (DMU) that were evaluated and hence gave more realistic results by discriminating the efficient and inefficient DMUs.

A Fuzzy Analysis Approach to Green-Resilient Supplier Selection in Electronic Manufacturing Systems

Adopting environmentally sound practices and utilizing resilient issues to face disruptive events and risks are new important agendas for enterprises, governments, and societies. The issue of supplier selection and evaluation in accordance with green and resilient principles has received little attention in the electronic industry. So, the present study aims to consider both criteria of resilience and greenness in a new supplier selection model. This study presents an integrated decision-making method based on the fuzzy set theory and data envelopment analysis (DEA) approach for supplier selection in a supply chain network in an electronic manufacturing company. The fuzzy set theory was used for analyzing suppliers' indicator weights, the suppliers' performance, and rating them with respect to subcriteria. Then, the obtained data were aggregated to classify and prepare them for use in a version assurance region of the DEA method with one dummy input and three output variables to rank the suppliers. The performance of the proposed DEA-based model has been verified through the sensitivity analysis and it has been validated by the existing FIS-based model in the literature. Furthermore, the proposed DEA-based model can be designed with any number of suppliers and criteria and subcriteria.

Towards a Triple Helix based efficiency index of innovation systems

This article presents a novel application of a two-phase Data Envelopment Analysis (DEA) for evaluating the efficiency of innovation systems based on the Triple Helix neo-evolutionary model. The authors identify a niche to measure Triple Helix-based efficiency of innovation systems scrutinizing different methodologies for measuring Triple Helix performance and indicating different perspectives on policy implications. The paper presents a new Triple Helix-based index that engages a comprehensive dataset and helps provide useful feedback to policymakers. It is based on a set of 19 indicators collected from the official reports of 34 OECD countries and applied in a two-phase DEA model: the indicators are aggregated into pillars according to the Assurance Region Global and DEA super-efficiency model; pillar scores are aggregated according to the Benefit-of-the-Doubt based DEA model. The results provide a rank of 34 countries outlining strengths and weaknesses of each observed innovation system. The research implies a variable set of weights to be a major advantage of DEA allowing less developed countries to excel in evaluating innovation systems efficiency. The results of Triple Helix efficiency index measurement presented in this paper help better account for the European Innovation Paradox.

Integrating quantitative and qualitative methodologies to build a national R&D plan using data envelopment analysis based on R&D stakeholders' perspectives.

The yearly increase in government R&D investment and top-down national R&D investment allocation requires a more quantitative decision-making system that maximizes R&D performance and efficient budget allocation. Sound decision-making is necessary at both the selection stage and the pursuit stage in order to maximize limited national R&D resources. We study Korean smart farms as an example to examine national R&D investment from the various R&D actors (academia, industry, and research institutes) perspectives. The objective of our research is to evaluate the theoretical efficiency of R&D investment on specific technologies in smart farms and compare the results with expert opinions where the reality is reflected. To be specific, our study is to provide the quantitative approach in making decision among policymakers by reflecting the field experiences and opinions. We use a data envelopment analysis with an assurance region model, which integrates an analytic hierarchy process and a data envelopment analysis. The weights of output in DEA model by the R&D actors are similar to the overall weight by all actors, implying that investment allocation decisions in the smart farm sector are not significantly affected by the R&D actors. We realized that the relative efficiency of some R&D technologies increases after reflecting qualitative ideas of experts. In reality, it is necessary to invest in these technology groups, but they excluded from top-down decision-making. This also shows that a government's top-down decision-making can distort its investment allocation. This study proposes a new approach to compensate for the difference between theoretical virtual prices and actual prices in data envelopment analysis. In particular, when comparing the only quantitative results on investment priorities with analysis results by additionally reflecting the opinions of experts in each sector, we found that the Korean government's investment priorities in the smart farm field are considerably distorted. Therefore, this study is expected to be used as an alternative for policy makers to compensate for the quantitative distortion might be caused by top-down national R&D investment decisions.

Spatial-temporal energy poverty analysis of China from subnational perspective

Alleviating energy poverty is a significant issue in many countries, for which effective energy poverty measurement is the prerequisite. This paper seeks to make a comprehensive and systematic assessment of the energy poverty performance of 30 provinces in China for 11-year period (2007–2017) by emphasizing their spatial-temporal changes. The paper firstly constructed energy poverty measurement conceptual framework based on overviewing previous studies. Secondly, a technique based on the integrated fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and Data Envelopment Analysis - Assurance Region (DEA-AR) for performance assessment was proposed. Then, the energy poverty performance was assessed, and clustering analysis was conducted to investigate the spatial-temporal changes of energy poverty in China. Finally, sensitivity analysis was applied for verifying the robustness of the proposed method. It is found that the energy poverty condition in China tends to be similar among most regions, but the gap between the best and worst performers gets wider. In addition, provinces with better economic conditions, have higher performance and are classified into the progressive group for alleviating energy poverty. On the contrary, provinces, whose economy lags, have regressed the most and are also the worst performers in reducing energy poverty. Thus, policies to improve household energy cleanability in high-income provinces and increase energy affordability in low-income regions would help reduce energy poverty in China.

An Approach for Generating Weights Using the Pairwise Comparison Matrix

Data envelopment analysis (DEA) has been used for obtaining weights for the analytic hierarchy process (AHP), an approach known as DEAHP. This method sometimes identifies more than one decision criterion or alternative as DEAHP-efficient. To overcome this problem, this paper proposes a new approach that not only generates appropriate weights for the decision criteria or alternatives, but also differentiates between DEAHP-efficient decision criteria or alternatives. To this end, we propose a DEA model with an assurance region and a cross-weight model that prioritizes decision criteria or alternatives by considering their most unfavorable weights. Two numerical examples are also provided to illustrate the advantages and potential applications of the proposed model.

Measuring the efficiency of university departments: an empirical study using data envelopment analysis and cluster analysis

Universities continue to play a key role in the development of a country. As university income streams shift away from government and as the income from admissions decline under the sub-replacement fertility phenomenon, the efficiency of resource utilization has become an important issue for university administrators. This paper applies data envelopment analysis (DEA) and the concept of an assurance region to evaluate the relative efficiency (including aggregate, technical, and scale efficiencies) of academic departments at National Chung Cheng University in Taiwan. The input factors considered are personnel (expressed in the number of faculty-equivalent persons), operating expenses, and floor area, and the output factors are teaching (expressed in total credit hours), publications (expressed in the number of papers), and external grants. Notably, teaching quality is included by considering the classroom capacity in calculating credit hours, and publication quality is included by considering the author contribution to calculate the single-author-equivalent numbers of papers. In addition, a cluster analysis based on the efficiency decomposition to the contributions of the three outputs is applied to classify the departments into three groups. The results of this paper not only provide the department head with the relative efficiency and improvement directions for the department but also serve as a reference for resource allocation and future strategy development for the university administration.

Solving Generalized DEA/AR Model With Fuzzy Data and Its Application to Evaluate the Performance of Manufacturing Enterprises

The use of conventional data envelopment analysis (DEA) models in real-world problems are limited because of some restrictions that must be considered in the model such as imprecise or vague data in inputs and outputs as well as additional information or assumptions. One way to handle this problem is by using fuzzy DEA with assurance regions (FDEA/AR) models. There is a common approach in almost all the suggested methods for solving FDEA/AR models. However, in this paper, we show that in some DEA/AR models, applying this approach can be led to inappropriate results. Four theorems are given to provide some sufficient conditions for a DMU to be the DEA/AR efficient. These theorems can be used to check the accuracy of the presented methods for solving FDEA/AR models, too. Moreover, a new method for solving a generalized FDEA/AR model that includes established DEA models such as CCR model (Charnes et al., 1978), BCC model (Banker et al., 1984), FG model (Fare & Grosskopf, 1985), and ST model (Seiford & Thrall, 1990) is proposed. These models are constant, variable, non-decreasing, and non-increasing returns to scale models, respectively. The proposed method is applied to evaluate the performance of manufacturing enterprises.

Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index

Energy security has become one of the growing concerns of many countries as well as the policy makers and decision-makers. This study aims to investigate the energy security performances of 30 Chinese provinces within the period of 2008–2017 by proposing a hybrid model that integrated Fuzzy Best-Worst Method, Data Envelopment Analysis and Assurance Regions. The dynamic trend of energy security performances of these provinces during this period has also been analyzed by using Malmquist Productivity Index. It was found that China’s provincial energy security has shown great variance, and the eastern and southern coastal provinces have experienced better energy security performance than that of the western and northern regions. In addition, China still improved its energy security performance during this period even it has been the country with the fastest growth in world energy consumption in recent decades. Specifically, energy security performance in the middle and western provinces seems to have made more progress than the coasts. Technological progress and energy innovations can help to improve China’s energy security by diversifying the energy supply sources/pathways and improving the energy efficiency, which is of great significance for the development of clean and sustainable energy.