Data Envelopment Analysis Model Research Articles

Evaluating renewable energy consumption efficiency and impact factors in Asia-pacific economic cooperation countries: A new approach of DEA with undesirable output model

Balancing economic growth with environmental protection is a global priority. Both developed and developing nations face the challenge of optimizing renewable energy use to achieve sustainable development while minimizing ecological harm. This study employs the Data Envelopment Analysis (DEA) model, incorporating undesirable output, to assess the efficiency of renewable energy consumption across 21 member countries of the Asia-Pacific Economic Cooperation (APEC) from 2011 to 2020. The main variables include foreign direct investment, total energy consumption, total renewable energy capacity as inputs, and gross domestic product growth and greenhouse gas emissions as desired outputs. Results reveal varying levels of renewable energy consumption efficiency, with Brunei, Japan, South Korea, and Hong Kong exhibiting the highest efficiency, while Russia, China, and Vietnam demonstrate the lowest. Notably, developed nations display greater efficiency in utilizing renewable energy compared to their developing counterparts. Moreover, optimizing foreign direct investment, renewable energy consumption, and total renewable energy capacity can enhance overall energy efficiency. By leveraging empirical evidence and estimation techniques, the study provides actionable recommendations for policymakers, emphasizing the pivotal role of financial development and green finance initiatives in promoting sustainable energy development across APEC member countries.

A Novel Pythagorean Approach Based Sine-Shaped Fuzzy Data Envelopment Analysis Model: An Assessment of Indian Public Sector Banks

A Novel Pythagorean Approach Based Sine-Shaped Fuzzy Data Envelopment Analysis Model: An Assessment of Indian Public Sector Banks

Service Innovation Performance in Sierra Leone's Banking Sector: An Empirical Analysis

This study investigates the innovation efficiency of Sierra Leone's banking sector, utilizing Data Envelopment Analysis (DEA) across 14 commercial banks representing 70% of the sector's total assets from 2017 to 2022. By analyzing existing financial and operational data, the study assesses performance variations and identifies disparities in innovation efficiency among these institutions. Findings indicate significant discrepancies in innovation efficiency levels, highlighting the critical role of strategic investment in technology and innovation for enhancing competitiveness and growth. This research contributes theoretically and practically to the literature on service innovation in banking in developing countries. It provides empirical evidence of the efficacy of DEA models in performance evaluation, offering benchmarks and strategic insights that could inform future policy and operational strategies. The study underscores the need for Sierra Leone banks to continually invest in innovative capabilities to maintain and enhance their competitive edge in a dynamic financial environment.

Medical equipment effectiveness evaluation model based on cone-constrained DEA and attention-based bi-LSTM

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies.

Rainfall’s impact on agricultural production and government poverty reduction efficiency in China

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Efficiency and Mergers and Acquisitions of Electric Utility Companies

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world’s largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&A buying transactions and M&A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&A transactions, they need to be more cautious about whether M&A transactions can provide value to the operation and technology management.

Assessing and enhancing operational efficiency in Sierra Leone's retail banking sector: A comparative analysis using CCR and BCC DEA models

AbstractThis study assesses the operational efficiency of 14 Sierra Leone commercial banks from 2020 to 2022, focusing on the adoption of financial innovation, such as digital banking, mobile services, and online payments. The research evaluates banks' performance by leveraging innovations using data envelope analysis (DEA) with constant and variable returns to scale models. Findings show varied efficiency, with some banks excelling and others lagging. The study underscores the need for strategic management and optimization so that banks can benefit fully from financial innovations. The insights inform policymakers and bank managers on enhancing the sector's operational performance through technology integration.

Strategic alliances and partnerships based on the semi-additive production technology in DEA

Strategic alliances and partnerships based on mergers and acquisitions can be used to improve the performance of decision-making units (DMUs). In this paper, we present different types of strategic alliance cooperation and partnership between DMUs and their corresponding aggregation DMUs. We propose two different strategies, we were able to carry out the process of strategic alliances and partnerships in semi-additive production technology. In the first strategy, two partner DMUs can compensate for the lack of resources in some of their input components by using excess input resources in some of each other's input components. Thus these DMUs can improve their efficiency score among all DMUs. The modeling of strategic alliances and partnerships in the first strategy was based on data envelopment analysis (DEA) models. In the second strategy, the two DMUs selected as partners adjust their input and output levels with the help of each other in such a way that these two DMUs achieve a target efficiency level predetermined by the DM. The modeling of strategic alliances and partnerships in the second strategy was based on inverse DEA models. We also apply the proposed approach to show importance strategic alliances and partnerships in banking. In the following, we compared the results of the models in carrying out the partnership process in two different technologies. The results showed that in semi-additive production technology, we can achieve potential gains for banks.

An inverse data envelopment analysis model for solving time substitution problems

This paper proposes new inverse data envelopment analysis (DEA) models considering input and output efficiency measures for the time substitution problem. We compare our DEA model with existing models in the literature. We show that existing models in the literature overestimate the required resources, and on the other hand, these are a special case of our models. We show that our models are unit invariant. We also describe another appealing characteristic of our model, that is, efficiency invariant, as a consequence of inverse structure. An illustrative example is provided to explain the proposed model and provide a comparison of our models and existing models in the literature. We also used our models in a real-world application, the selected economic sectors in Iran. The results show some potential improvements during the period of study.

A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks

In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.

Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased.

Impact of New Energy Industry Agglomeration on Green Innovation Efficiency—Based on the Regulative Effect of Green Finance

With the implementation of China’s innovation-driven high-quality economic development strategy, green and innovation are already the key factors of economic development. Therefore, developing green industry and improving regional green innovation have attracted wide attention and are of great significance to the sustainable development of China’s economy. Therefore, starting from China’s provincial panel from 2012 to 2021, this paper first uses the super-efficiency relaxation data envelopment analysis model (Super-SBM) to estimate green innovation efficiency (GI) and then uses the location entropy to measure the regional agglomeration level of the new energy industry (agg). Then, the generalized estimation of moments (GMM) model is used to explore the impact of agg on GI and verify the regulatory mechanism of green finance (GF). The results are as follows: (1) agg presents a distribution of “the highest in the eastern region, followed by the central region, and the lowest in the western region”, (2) agg can facilitate the improvement of GI, and in accordance with the threshold model, moderate GF will further amplify this effect. Therefore, the state and government should further promote the green finance policy, guide new energy enterprises to gather and contribute to the sustainable development of China’s economy.

Evaluating and enhancing natural resource asset management efficiency in China: A data envelopment analysis study

The efficiency of China's natural resource asset management system significantly influences the sustainable utilization of resources and the promotion of an environmentally sustainable economy. In this research, we employ a Data Envelopment Analysis (DEA) model to assess and analyze the efficiency and dynamic changes within China's natural resource asset management system. Our analysis is based on panel data collected from 30 provinces spanning the years 2007–2019. Our findings reveal that only three provinces demonstrated DEA effectiveness in managing natural resource assets, highlighting substantial regional disparities in efficiency. The presence of multiple institutions operating independently has led to a situation of overlapping responsibilities, resulting in critical issues related to redundant management. Inadequate resource tax collection intensity and a low tax utilization rate have contributed to the insufficiency in resource tax utilization. Local industrial structures and government priorities have hindered the active involvement of industrial enterprises in natural resource asset management in certain regions. We categorize changes in the efficiency of system operations in non-DEA-effective regions into three types: a temporary increase followed by a continuous decline, a gradual increase with overall low efficiency, and a rapid increase with relatively high overall efficiency. Consequently, this study offers policy recommendations aimed at enhancing the efficiency of natural resource asset management: (1) Clarify the ownership of natural resource assets and establish a well-defined hierarchical proxy system along with an integrated supervision framework. (2) Allocate resource tax revenue reasonably and promote collaborative coordination between resource taxes and other policy measures. (3) Strengthen the government's commitment to natural resource asset management, draw insights from advanced regions' experiences, and tailor policy approaches to the specific conditions of different regions to effectively balance economic development and environmental protection.

Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared resources

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

Input allocative efficiencies for operation and maintenance of rural piped water supply systems in highland areas of Vietnam

Strengthening the functioning of existing rural piped water supply systems is a critical strategy for ensuring household water security, particularly in water-scarce contexts. Improving operation and maintenance (O&M) of the systems is an important area of focus, commonly plagued by poor reliability and functionality over time. From an economic perspective, there is an opportunity to optimise O&M input efficiencies as a foundation for improved management. This paper presented challenges and opportunities to optimise O&M input efficiencies based on an analysis of water supply systems in Vietnam’s highland areas characterised by mountainous terrain and water scarcity. The analysis focused on state-based agencies for O&M given their mandate for restoring the inefficient systems and identified input norms for guidance on how to optimise O&M activities. We applied an input-oriented data envelopment analysis (DEA) model under constant returns to scale assumption to estimate technical, economic and allocative efficiencies. The results identified efficiency levels of 90%, 30% and 33% respectively. The study suggests a 10% reduction in general input amounts and identified efficient input target values reveal potential reduction rates for technical labour (12%), electricity (12%), as well as the technical and economic norms of technical labour (0.86 person- day∙(100 m3)–1 water sold) and electricity (0.53 kWh∙m–3 water sold). The policy implications for O&M state-based agencies include the adoption of input-based contracting mechanisms, while the government is encouraged to approve water tariffs and provide compensation based on input items to promote water service supply as a public good in water- scarce and challenging areas.

Spatial and temporal variation and convergence in the efficiency of high-standard farmland construction: Evidence in China

To assess the efficiency of high-standard farmland construction (HSFC), this study utilized data spanning from 1998 to 2020 obtained from 30 provinces, autonomous regions, and municipalities in China. This assessment was conducted using a three-stage super-efficient slack-based measure data envelopment analysis(SBM-DEA)model. Furthermore, we investigated the spatial and temporal variances and convergence of HSFC efficiency using the Dagum Gini coefficient and convergence analysis. The results firstly show that from 2007 to 2020, China's HSFC efficiency displayed fluctuations and declines, significantly influenced by environmental and stochastic factors. Secondly, it showed that the gap in China's HSFC efficiency has widened, with super-variable density being demonstrated as the primary source of spatial imbalance in HSFC efficiency. Thirdly, it showed that the deviation in the efficiency of HSFC in the central region of China from the average level has shown a gradually declining trend, while the deviation in the efficiency of HSFC in the national, western, eastern, and northeastern regions has not presented a downward trend. Fourthly, it demonstrated that over time, the efficiency of building high-standard agriculture throughout the nation and its four main areas will converge to the same level.

The driving force behind the growth of China's green economic efficiency: Provincial efficiency influence analysis for a sustainable future

The concept of green economic efficiency has attracted attention over the last decade. Several measurements of green economic efficiency and their various aspects have been proposed, and these have been the main focus in the literature. However, calculating the influence of each province on the country's green economic efficiency is essential to assist in targeted management and institutional design at the provincial level by the government. To perform such analysis, this study extended the data envelopment analysis model. Data from 30 provinces in China from 2010 to 2018 are used in the study. The results show that the provinces in the eastern and southern coastal regions have the greatest influence on the country's green economic efficiency, but the majority of the provinces in the Middle Yellow River region have the least influence on the entire green economy. Interestingly, high-efficiency provinces do not always exert the greatest influence, suggesting that even low-efficiency provinces can influence significantly on the economy. These findings can serve as a baseline for improving provincial influence and further promoting China's green economic efficiency.

Evaluating the technical efficiency of Egypt’s main container terminals

ABSTRACT Technical efficiency in container terminals refers to the ability of a port/terminal to operate at maximum capacity with a minimum number of resources. This includes optimising equipment and workforce utilisation, as well as streamlining operations and logistics to reduce delays and increase throughput. This research follows a deductive approach using Data Envelopment Analysis (DEA) models (CCR) and sensitivity analysis (SA) to define the most effective variable in the efficiency score of the main Egyptian container terminals. According to the sensitivity analysis, the most significant variable influencing the efficiency scores is the draft. Using the CCR model, it was found that the majority of the Egyptian container terminals under review were inefficient during the study period, with the exception of the East Port Said terminal. The current research is limited to evaluating the technical efficiency of six Egyptian container terminals (Alexandria, El-Dekheila, Damietta, East Port Said port, Port Said West, and El-Sokhna terminals) using two different DEA models from 2018 to 2022. The present research offers an in-depth approach for evaluating the technical efficiency of container terminals. The study’s findings could be applied to developing guidelines for enhancing the efficiency and productivity of Egypt’s container terminals.

CAMELS-DEA in assessing the role of major factors in achieving higher efficiency levels: evidence from Turkish banks

ABSTRACT This study investigates the efficiency of Turkish banks from 2010 to 2023 under unconventional central bank monetary policies, a departure from studies in developed economies using Capital adequacy, Asset quality, Management, Earnings, Liquidity, and Sensitivity (CAMELS) ratings. This study involves two stages. In the first stage, we calculate the efficiency scores of Turkish banks through a CAMELS ratings-based data envelopment analysis (CAMELS-DEA) model with two enhancements. Then, in the second stage, we conduct static and dynamic panel data analysis, relating these CAMELS-DEA efficiency estimates to factors and economy-wide variables thought to impact the banks’ efficiency levels, capturing cross-sectional variations across entities and mitigate potential endogeneity bias by incorporating lagged dependent variables as instruments. The study reveals a significant relationship between Turkish bank efficiency, size, and type, with public capital deposit banks as well as banks with the highest number of branches displaying superior performance. The results show that unemployment rate, government debt, exchange rate, and inflation rate have a meaningful impact on banking efficiency. Government debt and exchange rate have an inverse relationship with efficiency while the remaining variables are positively correlated. These findings underline the potential for rising inflation to trigger financial instability after abrupt and unforeseen inflation spikes.

Estimating the eco‐efficiency of urban waste services towards sustainable waste management

AbstractAssessing the eco‐efficiency of municipalities regarding the provision of solid waste services is a useful tool for improving its sustainability. However, robust, and reliable methods are needed to benchmark the performance of municipalities. Data envelopment analysis (DEA) methods typically used to assess the eco‐efficiency of municipal solid waste (MSW) service providers. However, the variables used in the eco‐efficiency assessment bear distinct weights for each individual unit, limiting thus interpretation. Therefore, benchmarking the performance of MSW service providers becomes challenging because different conditions are applied. Thus, this study uses a common set weights DEA (DEA‐CSW) model assess the eco‐efficiency of different municipalities in providing MSW services. Eco‐efficiency scores were estimated by integrating total costs, recycled waste and unsorted waste as input, desirable outputs and undesirable outputs, respectively. The empirical application of the model demonstrated that when using DEA‐CSW, only one municipality (out of 36) was eco‐efficient in managing MSW. The weightings for inputs and outputs were allocated based on the degree of satisfaction estimated for each municipality. Satisfaction ranged from 0.829 to 1.000 indicating that none of the municipalities were heavily penalized based on the eco‐efficiency scores estimation when allocating common weights. In conclusion, this study demonstrates that compared to traditional DEA models, the DEA‐CSW approach is more adequate at benchmarking the performance of municipalities regarding the provision of MSW services.