Data Envelopment Analysis Framework Research Articles

Soil quality evaluation for irrigated agroecological zones of Punjab, Pakistan: The Luenberger indicator approach

AbstractThis article describes the construction of the Luenberger soil quality indicator (SQI) using data on crop yield, non‐soil inputs, and soil profile from three irrigated agroecological zones of Punjab, Pakistan, namely, rice–wheat, maize–wheat–mix, and cotton–mix zones. Plot level data are used to construct a soil quality indicator by estimating directional distance functions within a data envelopment analysis (DEA) framework. We find that the SQI and crop yield relationships exhibit diminishing returns to improving soil quality levels. Using the constructed SQI values, we estimate linear regression models to generate weights that could be used directly to aggregate individual soil attributes into soil quality indicators without the necessity of fitting a frontier to the crop production data. For wheat and rice production, we find that SQI is most sensitive to changes in soil electrical conductivity (EC) and potassium (K). The SQI has direct relevance for site‐specific decision‐making problems where policymakers need to price land resources and conservation services to achieve agricultural and environmental goals.

An integrated multiple-criteria decision-making and data envelopment analysis framework for efficiency assessment in sustainable healthcare systems

Efficiency is critical in allocating sustainable healthcare resources to ensure that hospitals can effectively care for patients while maintaining high-quality care delivery. Hence, it is necessary to monitor efficiency carefully. This study aims to assess hospital unit effectiveness through a novel comprehensive approach integrating Multiple-Criteria Decision Making (MCDM) with Data Envelopment Analysis (DEA). The proposed MCDM-DEA framework involves allocating varying weights to distinct data categories. It harnesses the capabilities of the q-rung orthopair fuzzy (q-ROF) methodology to address the inherent uncertainties in healthcare performance assessment. The experimental results provide a comprehensively structured ranking system for specific hospital departments. This ranking system allows decision-makers to identify the strengths and weaknesses of each department, enabling them to make informed decisions regarding resource allocation and improvement strategies. Furthermore, the integration of MCDM-DEA provides a robust and objective assessment tool for monitoring and evaluating the performance of hospital departments over time. These rankings offer invaluable insights to decision-makers, equipping them with the strategic information needed to enhance the overall performance of hospital units.

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.

Strategic assessment of sustainable production in the international automobile sector using efficiency analysis and decision-making models

In the context of escalating global environmental concerns, the automotive industry faces pressing imperatives to align its growth with sustainable practices. This urgency, coupled with the need for a holistic evaluation framework, sets the stage for this study. Motivated by the gaps in quantitatively assessing sustainability, the study aims to discern and quantify the sustainability efforts of leading automotive manufacturers. The objective is to establish a robust, quantifiable link between operational efficiency and sustainability criteria that informs both business strategy and environmental stewardship. To achieve this, an innovative methodology combining the Malmquist Data Envelopment Analysis (Malmquist DEA) framework and the Ordinal Priority Approach (OPA) is employed. The approach analyzes efficiency changes, technological progress, and the industry's adoption of sustainable practices over a five-year period, concluding in the pre-pandemic year of 2020. The findings reveal a notable emphasis on productivity and market exposure, identifying them as pivotal to sustainable manufacturing. The OPA provides a weighted analysis of sustainability criteria based on expert evaluations, emphasizing the integration of environmental initiatives with core business strategies. The study's contribution is twofold: it introduces a novel, quantifiable approach to evaluating sustainability in the automotive industry, and it offers strategic insights for companies seeking to elevate their sustainability profile effectively. However, the reliance on pre-pandemic data and expert opinions marks the limitations of the study, indicating the necessity for further research. Future studies should aim to expand the temporal scope to include the latest industry shifts and to diversify the pool of sustainability criteria, capturing a broader spectrum of environmental and social dimensions. This continued research will enhance the industry's understanding of sustainable manufacturing in the evolving global context.

A new concept of education-innovation-economy-environment sustainability system: a new framework of strategy-based network data envelopment analysis

A new concept of education-innovation-economy-environment sustainability system: a new framework of strategy-based network data envelopment analysis

Northern Lights: Prospecting Efficiency in Europe’s Renewable Energy Sector

Northern European nations are at the forefront of renewable energy adoption but face challenges in optimizing energy conversion efficiency. There is a lack of detailed understanding of how behavioral factors affect the efficiency of renewable energy conversion in these countries. This study aims to evaluate and compare the renewable energy conversion efficiency of Northern European countries, intending to inform strategic policy making and identify best practices for technology deployment in the renewable energy sector. Employing a Data Envelopment Analysis (DEA) model, the study integrates behavioral economic parameters—specifically, the aversion loss and gain significance coefficients—to assess the efficiency of renewable energy conversion, accounting for psychological factors in decision making. A comprehensive sensitivity analysis was conducted, varying the gain significance coefficient while maintaining the aversion loss coefficient at constant levels. This experiment was designed to observe the impact of behavioral parameters on the efficiency ranking of each country. The analysis revealed that Latvia consistently ranked highest in efficiency, irrespective of the gain significance valuation, whereas Iceland consistently ranked lowest. Other countries demonstrated varying efficiency rankings with changes in gain significance, indicating different behavioral economic influences on their renewable energy sectors. Theoretically, the study enhances the DEA framework by integrating behavioral economics, offering a more holistic view of efficiency in renewable energy. Practically, it provides a benchmarking perspective that can guide policy and investment in renewable energy, with sensitivity analysis underscoring the importance of considering behavioral factors. The research offers a practical tool for policymakers and energy stakeholders to align renewable energy strategies with behavioral incentives, aiming to improve the adoption and effectiveness of these initiatives.

Convex or non-convex? A new meta-frontier data envelopment analysis framework considering technology compatibility

Convex or non-convex? A new meta-frontier data envelopment analysis framework considering technology compatibility

Estimating Marketing Efficiency of Organic Farmers: Evidence from Districts of West Bengal, India

The growing organic food market offers considerable trading opportunities for organic farmers in India. In this context, this article intends to find out whether the organic farmers are exploring these marketing opportunities in the three districts (namely, Purulia, Purba Bardhaman and Nadia) of West Bengal. Farm-level marketing efficiency is measured in a framework of data envelopment analysis. Evidence reveals that farmers’ marketing activities determine the level of marketing efficiency. Results show that organic farmers in Nadia district are relatively marketing efficient in comparison to the farmers in Purulia and Purba Bardhaman districts. Moreover, an attempt has been made to identify the determinants of marketing efficiency. The result shows that marketing efficiency is affected by the economic status, level of education, years of farming experience, size of landholding of the farmer household and the type of marketing arrangement.

A Novel Data Envelopment Analysis Framework for Performance Evaluation of European Road Transport Systems

The role of transportation is becoming increasingly important in the world economy, and road transport in particular plays a very important role in all types of transportation. For this reason, it is extremely important to monitor its performance regularly. Very often, this is done using Data Envelopment Analysis (DEA) performance evaluation models, and consequently, there are numerous articles in the literature on DEA evaluation of road transport systems. In this study, we first summarise these articles and classify them according to different characteristics (environmental, safety, economic, energy). Finally, we use them as a basis for developing a novel DEA framework, which is used for the evaluation of the efficiency and ranking of road transport systems that also takes into account undesirable outputs, i.e. environmental and safety outputs. As a case study, we evaluate 28 European countries from technical, safety and environmental aspects. The CCR and SBM models are used to evaluate the efficiency of these countries for the last two years of published data. The results show that Denmark ranks first and Cyprus last for both years. It was also found that safety efficiency is generally rated lower than other criteria. Finally, the results and reasons for the efficiency and inefficiency of specific decision-making units, i.e. countries, are discussed.

Productivity Decomposition of Chinese Energy Generation Enterprises: an Index Calculation Approach Embedded in the Data Envelopment Analysis Framework

To explore the development of China's energy power generation enterprises, undertake reasonable resource allocation and optimization, and promote sustainable development. We apply an index calculation approach within the data envelopment analysis framework, collecting data from 50 enterprises in 2007-2022, resulting in a total of 800 observations (16*50) for the in-depth analysis of productivity decomposition. The results highlight that during the study years, the sample enterprises experienced the following changes on average per year: an increase of 0.244% in efficiency change (effch), a decline of 0.293% in technical progress (techch), an increase of 0.119% in pure efficiency change (pech), an increase of 0.134% in scale efficiency change (sech), and a decline of 0.057% in total factor of productivity (tfpch). These findings suggest an ongoing improvement in overall efficiency, indicating that energy generators are achieving better results in terms of operations, resource utilization, and scale effects, but need to focus on research and adoption of new technologies while seeking opportunities to further enhance productivity.

A Comprehensive Farm Efficiency Analysis of Apple Growers in Newley Merged District South Waziristan

In the apple-producing land of South Waziristan’s three tehsils, Wana, Bermal, and Tiarza, this research paper is designed to analyze farmers' efficiencies, e.g., production, allocative and technical dimensions. Through a Data Envelopment Analysis (DEA) framework, the study evaluates the performance trajectories of 304 apple growers. A multistage sampling technique was used for the farmer’s interview. To the researcher’s knowledge, there is no detailed efficiency analysis of South Waziristan’s apple growers in existing literature, so there is a need to investigate the factors affecting efficiencies in the targeted area. The researcher is therefore encouraged to conduct a study of the farm efficiency of apple growers in South Waziristan. Apple growers' efficiencies were categorized as technical, allocative, and economic efficiency using the DEA approach. Results of the study show that there were tremendous inefficiencies of apple growers, which need to be reduced. This study sheds light on optimal resource allocation, productivity dynamics, and efficiency frontiers within apple cultivation in South Waziristan. The research offers an understanding of agricultural practices, resource utilization patterns, and efficiency benchmarks. The empirical insights derived from this comprehensive analysis facilitate informed decision-making among farmers and provide policymakers, agricultural stakeholders, and development practitioners with actionable recommendations to enhance agricultural productivity, sustainability, and socio-economic well-being in the region. Through empirical evidence and contextual relevance, this paper contributes significantly to the evolving discourse on agricultural efficiency analysis and underscores the imperative of tailored interventions for optimizing apple cultivation practices in South Waziristan.

A New Resource Allocation Multiple Criteria Decision-Making Method in a Two-Stage Inverse Data Envelopment Analysis Framework for the Sustainable Development of Chinese Commercial Banks

The resource allocation of commercial banks is a multiple-criteria decision-making issue with complex internal structure, and traditional inverse data envelopment analysis cannot meet its decision-making needs. A two-stage structure with undesirable outputs is constructed to describe the operations of a Chinese commercial bank, and then a new two-stage inverse data envelopment analysis with undesirable outputs is proposed to address its resource allocation multiple criteria decision-making issue. The new method can be used to calculate the minimum input increment required to achieve the goals of desirable and undesirable output under a certain efficiency, and then a specific resource allocation plan can be obtained to promote the sustainable development of commercial banks. Finally, the new method is applied to the resource allocation of 16 Chinese listed commercial banks in 2013, and the application results fully demonstrate the effectiveness of the new method.

Testing data envelopment analysis models on the performance of European Union regions in sustainable waste management

Achieving a Circular Economy requires effective waste management. Monitoring the transformation path taken by the European Union (EU) and its Member States is crucial for assessing the impact and effectiveness of current European waste legislation. Even though numerous studies have shown positive performance and convergence in waste treatment, regional efficiency evaluation studies are still lacking. This research aims to benchmark the performance of municipal solid waste management in 167 regions – at level 2 of the Nomenclature of Territorial Units for Statistics - across European Member States during the period from 2008 to 2013. It looks to assess whether there is any positive convergence among these regions. The efficiency assessment employs four different Data Envelopment Analysis models. One of these models, the Benefit-of-Doubt, is a non-parametric approach and represents a special case within the Data Envelopment Analysis framework. Our findings indicate a yearly reduction in the coefficient of variation of 3.6%. Despite this progressive convergence, substantial differences in municipal solid waste management performance exist between EU-20 regions, even within the same country. Particularly, the analysis demonstrates that the best performers are registered in the Central and Northern EU regions, while the Eastern and Southern EU regions performed worse.

Evaluating the Efficiencies of Logistics Centers with Fuzzy Logic: The Case of Turkey

The primary actor in today’s economic life, forming the backbone of the production-consumption cycle, is the distribution activities. Logistics centers (LCs) are organized areas where these activities are carried out together. Therefore, the efficiency and effectiveness of distribution activities are crucial for sustainability. This study incorporates fuzzy logic theory into the framework of data envelopment analysis (DEA) to measure the efficiency of LCs. Classical DEA assumes input and output data are precisely measured, making the efficiency scores unreliable and inconsistent when data precision is not always possible. The adoption of fuzzy logic is primarily to overcome possible uncertainties, errors, and ambiguities in data acquisition, preventing incorrect results. Hence, an approach assumes the data lie within specific intervals, was adopted to calculate the efficiencies of LCs based on α-cut levels. Officially obtained data on nine input and one output variable from twelve LCs operating in Turkey were used to calculate efficiency scores. As a result of the study, Köseköy/Izmit, Halkali/Istanbul, and Yenice/Mersin LCs were found to be fully efficient considering both lower and upper bound efficiencies. Moreover, the efficiency calculations using Fuzzy-DEA allowed for a more precise evaluation of LCs with high data sensitivity.

Identify more inefficiency sources to firm's innovation performance: low-value patent or R&D expenditure congestion?

ABSTRACTInnovation performance is critical to strengthening the market competitiveness of firms, but it would always be impaired by low-value patents and innovation resource waste. It is ideal for firms to identify the inefficiency sources in innovation performance and launch target improvements. Therefore, this study proposes a novel method framework of data envelopment analysis (DEA) to measure innovation performance and examine the inefficiencies caused by low-value patents and R&D expenditure waste. Under the proposed method, patent value is measured by the shadow price, and R&D expenditure congestion is identified as a form of waste. To prove its effectiveness, the method is applied to a case of 375 Chinese high-tech firms. Empirical findings are as follows. The presence of low-value patents and R&D expenditure waste are verified. Innovation performance is negatively affected by R&D expenditure waste but positively influenced by patent value. Moreover, higher patent value and greater R&D expenditure congestion ratio are found in firms with high-tech park locations, large sizes and international ownership, showing a positive correlation between patent value and R&D expenditure waste. The findings identify detailed inefficiency sources of firms’ innovations and provide solid managerial suggestions.

Aggregation of meta-technology ratio in DEA framework using the evidential reasoning approach

The metafrontier data envelopment analysis (DEA) model is a popular evaluation technique when different decision-making units (DMUs) may exhibit production technology heterogeneity. In this framework, the group metatechnology ratio (MTR) is an important indicator to help measure the technology gap at the group level. The common approach to the group MTR aggregation is the arithmetic average approach, which requires the MTR of the DMU to satisfy the ‘additive independence’ condition. Because the MTRs are generated from the same data set and linked to each other, the MTRs do not meet the ‘additive independence’ condition. Therefore, a new aggregation approach is needed. This study applies the evidential reasoning (ER) approach to aggregate the group MTR by the transformation of the MTR of DMU to pieces of evidence. Moreover, this study proposes examples that verify the applicability and practicality of the MTR aggregation using the ER approach, including an empirical example of the evaluation practice of the transportation system of 30 provincial regions in mainland China for 2013–2019.

Examining the efficiency of regional university technology transfer in China: A mixed-integer generalized data envelopment analysis framework

In university technology transfer performance assessment, some variables, such as labor and contracts, must be treated as integer-valued variables. Usually, the reference points obtained by the conventional data envelopment analysis (DEA) method are unrealistic when one of the input/output measures is an integer value. As a result, several integer DEA models have been proposed over the last decade. However, these models are based only on self-evaluation. In some cases, when a large gap exists between the best- and worst-performing units, the improvement path based on self-evaluation can be inappropriate. To overcome this issue, we propose a mixed-integer generalized DEA model based on peer evaluation to evaluate the efficiency level of regional university technology transfer in China. A sample of 27 provinces in China from 2008 to 2015 is used in the study. The results reveal that the eastern region, on average, obtained the highest level of efficiency among the three regions in China, whereas the western region presented a poor efficiency level. This is consistent with the current stage of development. Several recommendations are suggested for promoting technology transfer in the western region.

The government-production nexus of energy efficiency in China's construction industry: regional difference and factor analysis.

For decades, the construction industry has contributed significantly to China's economic growth. The heavy energy consumption inevitably leads to the release of large amounts of carbon emissions. Improving energy efficiency has been a crucial solution for mitigating the environmental impacts while boosting its green economy in the construction industry. Measuring the energy efficiency in the construction industry considering the quality of government sector is still limited. Using panel provincial data in China from 2011 to 2020, this paper proposes a two-stage dynamic data envelopment analysis (DEA) framework integrating the government sector with the production sector in the construction industry, and calculates energy efficiency. The spatial Durbin model is used to analyze the driving forces of energy efficiency. The research findings include (1) the energy efficiency in the eastern region is higher than that in the central and western regions. The mean values of energy efficiency in the eastern, central, and western regions are 0.42, 0.34, and 0.37. (2) Even though governance efficiency is lower than production efficiency, there is a positive correlation between governance efficiency and production efficiency with a correlation coefficient of 0.48. Improving governance efficiency is a significant step to increase the production efficiency and further increase energy efficiency of the construction industry. (3) Digital transformation has a positive effect on governance efficiency but has no effect on production efficiency. The government-production nexus framework provides implications for clarifying the role of government intervention in improving energy efficiency.

Possibilistic chance-constrained data envelopment analysis framework for failure modes and effects analysis

Failure modes and effects analysis (FMEA) is an intensively used risk analysis technique for prioritizing failure modes of products, systems, and services. Traditionally, FMEA assumes deterministic and precise values for risk factors. In engineering scenarios, the observed values of decision-makers on risk factors are, however, potentially stochastic and imprecise in nature due to the lack of adequate knowledge and experience. On the other hand, the structural correlation between risks (or failure modes) and induced factors (e.g., the loss cost and recovery time of products, systems, and services) is rarely reported in FMEA. To this end, we develop a possibilistic chance-constrained data envelopment analysis (DEA) framework to overcome the above deficiencies. In this framework, three differentiated categories of DEA approaches (namely, series, parallel, and network structure DEA) are devised to characterize such structural correlation. More specially, the Me measure is introduced to handle imprecise parameters associated with risk factors. Simultaneously, decision-makers are endowed to express flexible attitudes on these imprecise parameters. Moreover, in order to reveal the mechanism of risk priority of failure modes, the proposed framework enables decision-makers to offer optimistic and pessimistic attitudes on risk input and propagation, respectively. A case study of a production system, along with some comparisons and discussions, is given to demonstrate the effectiveness and merits of the proposed framework. As indicated from this case study, no matter which DEA approach is conducted, the risk priority of failure modes is stable in a stochastic environment.

Breaking ground in ESG assessment: Integrated DEA and MCDM framework with spherical fuzzy sets for Vietnam's wire and cable sector

In corporate sustainability reporting, the credibility of environmental, social, and governance (ESG) information is paramount, especially for socially responsible investors relying on ESG metrics. This study proposes an integrated method combining Data Envelopment Analysis (DEA), Analytic Hierarchy Process (SF-AHP), and Weighted Aggregated Sum Product Assessment (SF-WASPAS) under the Spherical fuzzy environment to assess the ESG performance of Vietnam's wire and cable companies by integrating quantitative indicators from financial statements and qualitative factors from literature reviews and expert interviews. Firstly, the DEA-Malmquist model gauges the efficiency performance of the ten wire and cable enterprises through five critical financial indicators. Secondly, using expert opinions, the SF-AHP method determines the weights of 16 selected ESG criteria, offering a qualitative dimension to the evaluation. Subsequently, the SF-WASPAS approach ranks the ten businesses based on aggregated scores derived from the criteria, forming a robust foundation for ranking. Furthermore, a comparative analysis demonstrates the practicality and reliability of our proposed framework against the Technique for Order of Preference by Similarity to Ideal Solution (SF-TOPSIS) and Fermatean fuzzy WASPAS. While the Malmquist results highlight SAM Holding Corporation (A6) with the highest average efficiency score, a holistic assessment across all three ranking methods places Vietnam Electric Cable Corporation (A1) at the forefront, closely followed by Hai Phong Electronic Mechanical JSC (A2). By harmoniously blending quantitative and qualitative perspectives and employing robust evaluation techniques, this study extends to effective resource management, targeted investor attraction, and heightened stakeholder engagement, offering substantial benefits to the wire and cable industry's ESG endeavours.