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This paper investigates the impact of credit risk shocks on the evolution of banking efficiency in China.

This paper introduces credit risk as a bad output into a bootstrap data envelopment analysis (bootstrap-DEA) model.

During a credit risk shock, the efficiency levels of both state-owned commercial banks and joint-stock commercial banks are significantly higher than those of urban/rural commercial banks, and the efficiency differences between these banks further increase during a period of economic slowdown. This paper also finds that the efficiencies of joint-stock commercial banks are the most sensitive to credit risk shocks; these banks are the first to be affected and the first to completely adjust. However, urban/rural commercial banks adjust very slowly.

Most scholars still use the traditional DEA method to estimate China's banking efficiency. The bootstrap-DEA method is clearly able to obtain a more exact estimated efficiency score. In fact, in comparison with the bootstrap-DEA model, we found that the traditional DEA method overestimates China's banking efficiency, and this is an especially serious problem for those banks that have a high efficiency score.

Traditionally, economic production models consider pollution as bads that may be modeled as either outputs or inputs in economic models. The purpose of this paper is to examine the implications of these modeling choices on the measurements of productive efficiency and private costs of pollution control.

The authors apply the hyperbolic distance functions to measure trucking efficiency and the private costs of pollution control.

The results show: (i) regardless of the choice of modeling, when only one bad was incorporated in hyperbolic distance functions, the efficiency loss and private abatement cost measures derived from the two models were equivalent, but potential pollution reduction and good output expansion differed; (ii) when more than one bad were introduced, the equivalence of efficiency loss measure in (i) did not hold; and (iii) the potential amounts of pollution reduction and good output expansion were larger when bads were modeled as inputs. With multiple bads, private abatement costs varied considerably under the two modeling treatments.

From a policy standpoint, the results suggest that one should consider the modeling options with caution when multiple economic bads are involved, because the resulting measures of economic burden of pollution control differ.

The paper shows that the traditional conceptual framework for modeling pollution in hyperbolic distance functions could yield inconsistent results.

The objective of this paper is to propose a new data envelopment analysis (DEA) model for assessing sustainability of suppliers with partial impacts between inputs, desirable outputs and undesirable outputs.

This paper examines partial impacts of inputs on desirable and undesirable outputs and applies weak disposability assumption to propose a novel DEA model to determine the sustainability of suppliers.

This paper shows the type of resource sharing in DEA models and takes into account sustainable development and sustainability assessment concepts for sustainable supplier selection problem and develops a DEA model for selecting the most sustainable suppliers with partial sharing of resources. To select the most sustainable suppliers, this model helps managers to consider aggregate efficiency, overall efficiency and bundle efficiency. The paper introduces the supplier which is efficient at all levels as the most sustainable supplier.

For the first time, this paper suggests a new DEA model by partial impact between inputs and good outputs/bad outputs for selecting sustainable supplier and deals with the situations in which each supplier has several subunits. The new model calculates aggregate efficiency, overall efficiency and subunit efficiency of supplier. paper introduces the supplier which is efficient in all levels including aggregate efficiency, overall efficiency and subunit efficiency as the best supplier.

The purpose of this paper is introducing an alternative model to measure the relative efficiency of observations with undesirable products. Describing the reference set and benchmarking.

In this paper, an alternative definition of weak disposability assumption is introduced to handle undesirable outputs. Actually, two types of undesirable outputs are addressed and a substitute definition of weak disposability is presented.

Using this assumption a linear production technology set along with a performance analysis model is constructed to assess the relative efficiency of the decision-making units. To illustrate the radial application of the proposed approach, a real case on transportation system of USA during 1992-2009 is given.

To date, data envelopment analysis studies have investigated undesirable outputs by the assumption of weak disposability, defined as the proportional contraction of good and bad products, which leads to the null-joint assumption between good and bad outputs. Therefore, the only way to produce no undesirable outputs is producing zero desirable outputs. So the production process should be stopped while it is not economically cost-effective. However, in some processes there are some undesirable outputs, which are decreased with non-same percentages. So these undesirable outputs can be stopped while the good outputs have a strictly positive value. In this situation, the good outputs are not null-joint with this type of bad outputs. In the current paper, a new definition of the weak disposability of outputs was represented while two groups of undesirable outputs were considered. Hence, desirable outputs and the first kind of undesirable outputs were decreased proportionally. However, the reduction value was different for the second kind of undesirable outputs. Hence, the null-joint assumption is removed from the production technology. Then, a new technology was proposed based on five postulates as inclusion of observations, free disposability of desirable outputs and inputs, new weak disposability, convexity and minimum extrapolation.

This paper studies the efficiency of Indian coal-fired thermal power plants (CTPPs) in by-production of electricity and particulates also known as Suspended Particulate Matter (SPM).

A non-radial directional distance function is optimized using data envelopment analysis to enumerate the overall inefficiency of CTPPs and its components in recent times. Further, second-stage regression analysis is conducted to identify factors that affect the inefficiency of plants.

The low inefficiency score for electricity generation suggests that most CTPPs operate close to the good output frontier. A high degree of emissions inefficiency is a challenge for Indian CTPPs. Ever-rising coal use inefficiency is a hindrance to control SPM emissions. The second stage regression analysis concludes that factors like ownership and capacity utilization play vital roles in determining a plant’s inefficiency level. Privately owned CTPPs have performed better in terms of technical inefficiency and emission inefficiency than plants owned by Central and State governments.

To the best of the authors’ knowledge, this study is one of the few published works that benchmark the productive and environmental performance of Indian CTPPs.

Due to stringent regulations on carbon emissions, green manufacturing has become a critical issue in manufacturers’ strategic planning. Manufacturers are greening production through carbon abatement activities. The purpose of this paper is to investigate the factors that influence the effects of carbon abatement on environmental productivity growth.

Using data envelopment analysis with directional distance function, this study examines productivity growth associated with carbon abatement under regulated and unregulated production technologies. A pollution abatement index (PAI) is constructed for determining the effects of carbon abatement on environmental productivity growth. Panel data of 18 European countries in paper and pulp and coke sectors are collected for the analysis.

The empirical findings reveal that carbon abatement may positively or negatively affect environmental productivity growth which is dependent on the nature of technology in a sector, the innovation capabilities of a country and environmental regulations.

Conventional approaches in measuring productivity changes do not normally take undesired outputs (e.g. carbon emissions) into consideration. This study contributes to literature by constructing a PAI that considers productivity changes under a joint production technology (where both desired and undesired outputs are considered). The findings enhance current understandings on the effectiveness of carbon abatement activities and help managers establish corporate environmental strategies to adopt green manufacturing.

The paper investigates the production inefficiency of the US electricity industry in the wake of restructuring and emission reduction regulations.

The study estimates a multiple-input, multiple-output directional distance function, using six inputs: fuel, labor, capital and annualized capital costs of sulfur dioxide (SO₂₎, nitrogen oxides (NO_X) and particulate removal devices, two good outputs – residential and industrial-commercial electricity and three bad outputs – SO₂, carbon dioxide (CO₂) and NO_X emissions.

The authors find that restructuring in electricity markets improves deregulated utilities' technical efficiency (TE). Deregulated utilities with below-average NO_X control equipment tend to invest less in these devices, but above-average utilities do the opposite. The reverse applies to particulate removal devices. The whole sample spends more on NO_X, particulate and SO₂ control systems and reduces its electricity sales slightly. Increased investments in SO₂ and NO_X control equipment do not reduce SO₂ and NO_X emissions, but expansions of particulate control systems cut down SO₂ emissions greatly. Stricter environmental regulations have probably shifted the production frontier inwards and the utilities farther from the frontier over time.

Restructuring and environmental regulations do not make all utilities invest more in emission control systems. The US government should devise other schemes to achieve this goal.

The paper unveils heterogeneous reactions of US electric utilities in the wake of restructuring and emission regulations.

Operational performance is critical for the banking sector for both managers and other stakeholders as it strongly affects the overall performance of the banking system. Traditional performance measures such as ratio analysis encountered certain shortcomings. At this juncture, data envelopment analysis (DEA) approaches are increasingly applied in bank efficiency studies. However, basic DEA models ignored the interactions between consecutive terms and focused primarily on measuring performance independently for each study period. All this is required to develop an operational performance model that can enable the long-term decision model.

An attempt has been made to develop a dynamic DEA within a non-radial category to measure interconnection activities considering non-performing loans as an undesirable link. This study uses the Indian banking dataset from 2015 to 2019. The study's research design directs three directions: ‘comparison of the dynamic DEA with the traditional static DEA model, areas of inefficiencies that are investigated for each factor using the factor efficiency index and the robustness results highlighting the performance difference between bank categories.'

Comparing with static DEA results, the study confirms that the dynamic model best measures long-term operational performance due to the linkage between consecutive terms. The efficiency analysis concludes that the input factor that requires the most improvement is ‘fixed assets' and ‘deposits'. The output factor that needs the most progress is ‘non-interest income'. The robustness of the developed model is proven by ownership categories present within the Indian banking system. At a significance level of 10%, the result of both the separate and dynamic model for privately owned banks is significantly better than that of publicly owned banks.

This paper proposes an operational efficiency model for Indian banks in line with undesirable output. The mean factor efficiency analysis related to non-radial DEA modelling enhances managerial flexibilities in determining improvement initiatives.

There is ample empirical evidence to show that larger banks are more efficient than smaller banks in developed countries. However, there is very little empirical evidence to show that in small developing economies, such as Vietnam, bank size is associated with increased risk, especially credit risk. This paper aim to provide empirical evidence to fill in this gap. This paper employs a slack-based directional distance function using the intermediation approach in measuring the inefficiency of banks in Vietnam during the period 2006–2015. Non-performing loans are used as an undesirable output to capture credit risk. The results show that small banks are more efficient than large banks at the mean level and across the entire distributions of inefficiency of the two groups. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies. The study’s empirical results contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to enhance the performance of Vietnam's banking system as a whole.

This paper uses the non-radial slack-based directional technology distance function developed by Färe and Grosskopf (2010) to estimate the efficiency of banks using the data envelopment analysis technique. Data for 44 commercial banks are used.

The empirical results of the paper contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to improve the performance of Vietnam's banking system as a whole.

This paper extends the extant literature by examining whether efficiency is associated with size in a typical transitional developing economy. The classic Cournot model, the structure-conduct-performance and the efficiency structure hypotheses state that larger banks are more efficient than smaller banks (Bikker and Bos, 2008). Empirical studies of Berger (2003), Mester (2005), Wheelock and Wilson (2012) lend support to the statement in developed countries. However, not much empirical literature focuses on small developing economies such as Vietnam to show that bank size is associated with increased risk, especially credit risk. The study’s empirical results show that size enlargement is not positively associated with risk-adjusted efficiency. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies.

This paper presents an alternative approach to evaluating the overall efficiency and performance of Taiwanese container ports. Specifically, a parallel activity with series structure concept in the form of data envelopment analysis (MNDEA) is used to construct a model that applies to three different activities: harbor management, stevedoring and warehousing operations. We will further divide each activity into two process types, production processes and services processes. We will also adopt a Delphi survey approach and use the Analytic Network Process (ANP) to identify these processes’influence dependence and their degree of importance for the MNDEA model setting. An empirical application demonstrates the performance of Taiwanese container ports by using MNDEA with window analysis techniques via the directional distance functionThe results demonstrate that the application is effective in indicating and/or suggesting resource-adjustments, while considering which undesirable output levels and shared inputs were involved. The results also present directions for possible improvements in workplace efficiency.