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In this article, we analyse India's transport sector policy. The study examines the two important modes of transport, viz., road and rail, and analyses the efficiency of transport infrastructure from 2011 to 2019. Through Data Envelopment Analysis (DEA), we evaluate modes in terms of the capacity of registered road goods vehicles and railway goods wagons, truck km/train km and energy consumption and corresponding freight traffic. The analysis gives valuable insight into the fact that rail infrastructure is more efficient than road infrastructure. Still, current transport sector policies promote energy-intensive carbon-fuel road vehicles rather than energy-efficient alternative transport modes. The study suggests the changes that may be incorporated in inputs to improve the efficiency of the road infrastructure. The Data Envelopment Analysis (DEA)-Malmquist Index analysis reports the need for total factor productivity improvement in rail transport and the introduction of technological innovations in road and rail transport sector management. The study employs a well-to-wheel (WTW) based index to ascertain the energy consumed and pollutants emitted by freight-carrying road and rail modes between two leading business hubs of India, i.e., Ludhiana and Mumbai. The paper identifies policy gaps and suggests a holistic approach for need-based roadway investment and a time-bound development of railway infrastructure in India. Our study generates valuable implications for India's policy planners and government authorities and provides new insights to other countries. • The study examines road and rail transport, and analyses the efficiency and energy consumption of these modes. • The study suggests the changes to improve the efficiency of the road infrastructure. • The Data Envelopment Analysis (DEA)-Malmquist Index analysis and well-to-wheel (WTW) based index have been employed for the analysis. • The paper suggests a need-based roadway investment approach and a time-bound development of railway infrastructure. [ABSTRACT FROM AUTHOR]

The abundance of country-level socio-economic development indices is of limited help unless sub-country-level indices on the quality of policies are also available. Tracing the regional indices landscape of India, this study proposes the state-level Environment Policy Stringency Index for Indian states. Capturing Indian states' policies across six areas with weight determination done deploying principal component analysis and Data Envelopment Analysis-Benefit of Doubt approach, the composite index calculated in the study also examines the scores grouping the sub-national units based upon the income levels. Besides ranking for inter-state comparison, the proposed index is significantly associated with particulate matter pollution levels in states. This study has three key contributions: conceptualising the principal-based sub-national index while making a case for developing sub-country level policy indices, identifying state-level environment policy variables and constructing an Environment Policy Stringency Index for Indian states. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

Neutrosophic data envelopment analysis (Neu-DEA) is a mathematical tool for calculating the performance of decision-making units (DMUs) in a neutrosophic environment. In this paper, a novel ranking function is developed specifically for single-valued trapezoidal neutrosophic numbers (SVTrNNs). The properties of this new ranking function have also been extensively studied. We develop the Charnes–Cooper–Rhodes models in the context of SVTrNNs to measure the efficiency of homogeneous DMUs. Additionally, we propose a unique solution process by incorporating the newly developed ranking function, which allows for the conversion of the neutrosophic DEA model into a corresponding Crisp LP model. This approach offers an efficient and effective tool of measuring the DMU efficiency in a neutrosophic environment. The reference set or peer group is defined for inefficient DMUs, which enables them to improve their efficiency score by following their peers. Two different approaches are suggested as potential ways to completely rank the DMUs, i.e., the reference set- and the super-efficiency-score-based ranking approaches. The validity and existence of the proposed models are demonstrated by providing an illustrative example, and the results are compared to an existing approach. The proposed Neu-DEA model is employed to assess the efficiency of the 12 major seaports in India, and it yields superior outcomes compared to the currently existing models. [ABSTRACT FROM AUTHOR]

Objective: This research paper aims to evaluate the relative efficacy of pharmacognosy research on the top 40 tertiary hospitals and its pharmacy in India according to Newsweek's 2021 ranking. Purpose: India's pharmaceutical market is now estimated to be worth $50 billion. India is a significant pharmaceutical exporter, reaching more than 200 nations with its exports. This study will help to identify whether the pharma industry is working efficiently in selected Indian hospitals or not. Thus, to create a bench marking for the hospital pharmacies in India. Design/methodology/approach: Data for the study came from public and private hospitals' official websites, financial records, and government notifications. Crosssectional data analyses were done using a Slack-Based model and data envelopment analysis (DEA). STATA was the software used to scrutinize the results effectively. Results: Thirteen hospital pharmacies out of a total of 40 are judged to be relatively technically efficient overall, according to the research. The findings of the slack analysis demonstrate that government hospitals and pharmacies are better able to handle slack and effectively manage input and output. Conclusion: The researchers also set upped a benchmarking hospital both for private and government healthcare settings for pharmacognosy research. Thus, it can be utilized for the betterment of the pharmacies. [ABSTRACT FROM AUTHOR]

The cost and profit efficiency of all Indian life insurance businesses is examined in this article using the data development analysis technique for the years 2017 through 2021. The Tobit regression is also used in this study to look into how well life insurance performs in relation to company factors. Cost efficiency scores were higher than profit efficiency ratings, as shown by the study. According to this, one of the most important inefficiencies for Indian life insurance is revenue inefficiency. In addition, state-owned life insurance firm in India is more profitable and economical than private ones, according to this study. In general, joint life insurers are more cost-effective than domestic life insurance companies, whereas domestic life insurance companies are more profit-efficient than joint venture insurers. The profitability and cost-effectiveness of life insurers are positively impacted by the claims ratio. While there are favorable effects of the distribution ratio and market share, respectively, effects on profit and cost-effectiveness. During the study period, the Indian life insurance businesses were relatively cost and profit efficient, according to empirical evidence. This study is the first to examine the profit and cost efficiencies of all Indian life insurance businesses. Decision-makers, top management, regulators, and other industry stakeholders will all gain from the research. [ABSTRACT FROM AUTHOR]

Revenue risk is the most critical factor that hampers the long-term viability of highway infrastructure service projects. This paper develops an integrated multi-method decision framework to evaluate and compare multiple revenue risk mitigation strategies for build-operate-transfer (BOT) highway infrastructure concessions. Using the project data of a real highway BOT case project in India, this paper models and evaluates various risk mitigation strategies on different performance metrics using Monte Carlo simulation analysis. The technical efficiencies of the risk mitigation strategies are estimated using data envelopment analysis (DEA) and efficient strategies are identified. The findings suggest that strategies comprising appropriate combinations of minimum revenue guarantee, excess revenue share, and revenue shortfall loan mitigate the revenue risk most efficiently. The decision framework and the findings facilitate efficient public infrastructure contract design by policymakers and set multiple research directions. [ABSTRACT FROM AUTHOR]

Across the world, achieving sustainable water resource development and managing limited natural resources like water have proven to be significant issues, and India is no exception to the same. At present, irrigation consumes about 80 percent of the total available water in India and is predicted to remain the dominant water user. India is already a country with water stress and is on its way to becoming a country with water scarcity. Rising agricultural productivity, expanding urbanisation, growing industrialisation, and accelerating industry create competing demands for water. Most irrigation system issues in India are attributable to poor management of the built infrastructure. The present study aims to rank the irrigation canals based on their efficiencies and inefficiencies to improve the overall performance of WUAs in Jharkhand. The ranking is done for nine irrigation canals based on financial inputs like investments in training and development of WUAs, maintenance and repairs of canals, and outputs like users' charges collection and yield in crop production. The Comprehensive Efficiency score analysis suggests that while there is room for improvement in the performance, the Water Users' Associations have generally been successful in improving the technical efficiency of the irrigation system. The findings can be used for further investigation to identify best practises and areas for improvement. [ABSTRACT FROM AUTHOR]

With growing environmental legislation and growing public awareness of the importance of pursuing sustainable growth, developed nations are increasingly recognizng the value of waste reduction, recycling, and reuse maximisation. MSWM efficiency is the focus of this study, which also attempts to identify the most effective ways to put MSWM policies into practice and make significant strides in increasing MSWM efficiency. This empirical study examines the ecological and economic performance of MSWM for 26 major cities of Madhya Pradesh state, India in 2016 to 2020. A non-parametric approach, Data Envelopment Analysis (DEA) method has been employed. More precisely, output-oriented DEA models have been used to analyse both constant and variable returns to scale in the context of environmental performance. The results from the DEA CRS model indicate that 12 units were efficient in 2016-2017, 10 units in 2017-2018, 13 units in 2018-2019, and 16 units in 2019-2020. In contrast, the DEA VRS model predicts that the number of efficient units were from 16 in 2016-17 to 14 in 2017-18 to 17 in 2018-19 and 19 in 2019-2020. Additionally, the Shapiro-Wilk normality test shows that the distribution of efficiency scores is not normal. Out of the 26 units analysed, 15 have an efficiency score of 1, indicating they are technically efficient. The mean efficiency score for all units is 0.959, with 57.7% of units having an efficiency score of 1. Only 3.8% of units have an efficiency score between 0.7 and 0.8. The study finds that the Northern and Central large cities have higher efficiencies compared to the Eastern and Southern regions. Cities in the north, centre, and south had Potentially Toxic Elements (PTEs) that were comparatively less. The PTE of western cities was comparably more, while the eastern region's efficiency distribution was dispersed. [ABSTRACT FROM AUTHOR]

The Indian Railways is India's biggest employer and undeniably influences the country's transportation network, economy, and social and cultural systems. The network is split into zones for operational reasons. It is vital to evaluate these railway networks to identify their strengths and shortcomings and to improve their performance. Past works often use Data Envelopment Analysis to evaluate railway services. Our contribution lies in the inclusion of several aspects not previously considered, such as (i) both tangible and intangible criteria, (ii) the hierarchical nature of the problem, and (iii) additional useful criteria and data to analyze the performance of the zones, including physical assets, operating ratio, accidents, comfort, travel experience, flexibility, transparency, etc. We use the novel Hierarchical Fuzzy Axiomatic Design method to evaluate the performance of sixteen zones in the Indian Railways since it suits our problem well. We find that the Southern Railway zone performs best, while the Northeast Frontier zone is ranked last. We also identify the strengths and weaknesses of all railway zones. [ABSTRACT FROM AUTHOR]

Background: Efficiency of production system is calculated by the input spent to amount of output produced. The Agronomic efficiency has been found to be below 48% than the world efficiency standard of 40 kg ha-1. The average fertilizer usage of India is 133 kg ha-1, 232% higher than the global standard fertilizer of 40 kg ha-1. The certified seeds and agriculture machinery usage has been increased by 15% and 13% in India but the efficiency of farm stays behind. Methods: With this back ground the present study applied cost approach variable return to scale Data Envelopment Analysis (DEA) model to evaluate the Indian farms resource using 200 rural farms of Tamil Nadu state. The data envelopment analysis models have the advantage of simultaneous measure of technical, allocative and cost efficiency of the individual farms. Result: The cost efficiency among the respondents ranged from 0 to 1 with a mean efficiency of 0.66. The study found that Decision-Making Units (DMU) have cost inefficiency, suggesting that most of the DMU operated very far away from the efficiency frontier. Allocative inefficiency (0.55) is worse than the technical inefficiency (0.80), implying that the lower economic efficiency is the result of high allocative inefficiency. [ABSTRACT FROM AUTHOR]

This study uses three data envelopment analysis models to determine the production efficiency of biohydrogen which is produced from macroalgae and other sources by dark fermentation. The efficiency of macroalgae is greatest in batch mode for S. Japonica using a sDFMEC process at pH 5.3, 35 °C, 1 g COD/L and a hydrogen production rate (HPR) of 0.34 L/L/h. The highest efficiency is using an internal circulation batch reactor in continuous mode for beverage waste water. The HPR and substrate concentration are the most important factor of biohydrogen efficiency, and efficiency and temperature are the most important factors of HPR. Malaysia and India are the two economies that most benefit from increased production efficiency due to the use of macroalgae. Increasing biohydrogen yield efficiency will improve macroeconomic growth and establish a renewable hydrogen and biohydrogen industry, which is especially efficient related to the economic recovery during the COVID-19 pandemic. • Determining production efficiency by dark fermentation of bioH2 from macroalgae •S. Japonica/sDFMEC/1g COD/g/pH 5.3/35 °C is the most efficient combination •The HPR and SC are the most important factor of biohydrogen efficiency •Improved efficiency will give Malaysia and India the greatest benefit in bioH2 •Investment on bioH2 efficiency technology will increase bioH2 output [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate the technical efficiency of Indian pension funds using data envelopment analysis (DEA) and to assess the reasons of inefficiency if any. This article analyzes the efficiency performance of all the pension funds available to Indian subscribers from the year 2015–2019 using radial measurers (BCC) of DEA based on secondary data collected from the annual reports of New Pension System Trust. Findings indicate that the average efficiency of Indian pension funds was 75.38% and this sector experienced overall stability in the average efficiency levels during the study period. Almost 40% of the pension funds operated efficiently in one or more years during the study period. The minimum slack was found in the input "expense ratio." This confirms that risk associated with investments is the cause of inefficiency and not the expense ratio in the Indian pension sector. Tobit regression is applied to explore the main drivers of efficiency in the India pension funds. The study finds that fund size has positive association with the efficiency of the pension funds. Public sector funds were more efficient than the private sector funds. This study is first of its kind that has assessed the efficiency of Indian pension funds. The study brings into light the operating characteristics and efficiencies of the Indian pension funds for the period 2015–2019 and therefore holds important insights for policy makers, practitioners, and decision‐makers. [ABSTRACT FROM AUTHOR]

Background: India is the largest producer as well as consumer of pulses in the world due to higher relance of their people on vegetarian sources of proteins, however, we have been dependent on import of pulses for meeting our demand for pulses over several decades. As a strategy to be self-reliant in pulses production two important initiatives were taken by the government of India viz. implementation of Accelerated Pulses Production Program (A3P) and inclusion of pulses in the National Food Security Mission (NFSM-Pulses) during 2010-11. Pigeon pea has tremendous medicinal and pharmacological properties and uses in addition to the good quality source of proteins. Significant improvement observed in production and productivity of pigeon pea after the A3P and NFSM-Pulses was questioned for its Total Factor Productivity (TFP) growth. Methods: This study was designed to assess impact of A3P and NFSM-Pulses on TFP of Pigeon pea in major Indian producing states. TFP was estimated with the help of Malmquist Productivity Index (MPI) using Data Envelopment Analysis Program (DEAP) version-2.1 in five states of India viz., Gujarat, Karnataka, MP, Maharashtra and UP which contribute more than three-fourth pigeon pea production in India. Result: TFP change in period-II (after A3P and NFSM-Pulses) was invariably higher than the TFP change in period-I (before the initiatives) in all the studied states. It indicates that these government initiatives contributed to the enhancement of production and productivity of pigeon pea with the support of better technology adoption rather than just led by higher use of inputs. [ABSTRACT FROM AUTHOR]

The paper adopts the conventional data envelopment analysis (DEA) and bootstrap procedure to analyze the technical efficiency, while tobit regression is applied to identify the factors affecting efficiencies of exotic fish polyculture in Jammu and Kashmir (India). According to the statistical analysis of the variables utilized, there was a lot of variability in the inputs being used by the farmers, with the most variation being in the lime input. The DEA estimated technical efficiency for the sample farms in Jammu and Kashmir is 0.9771 and 0.9741, respectively, with least technical inefficiency of 3%. The bias-corrected (bootstrapped) technical efficiencies found were slightly lower than the ones estimated by conventional DEA. Mean allocative and cost efficiencies for sample farms in the Jammu region were 92% and 75%, respectively, and 84% and 74%, respectively, for farmers in the Kashmir region. Farming experience, age, and education have favorable impacts on technical efficiency of farmers in the state, but family size showed negative impacts. Efficiency improvement will eventually lead to increase in the production providing better scope for marketed surplus. More fisheries extension is suggested for expanding the exotic fish culture in the state making the enterprise a more profitable venture. [ABSTRACT FROM AUTHOR]

This study examines internal greener operations in Indian healthcare during COVID-19 pandemic. Specifically, the research explores the interrelationships among three parallel healthcare lines: general care, emergency care, and biomedical waste (BMW) management to fulfill greener healthcare's targeted sustainable development goals. We use the three parallel stage data envelopment analysis (DEA) approach model to assess efficiencies of the overall system and the individual parallel stages. We collected panel data for cities in the state of Maharashtra, India during the second wave of pandemic in 2021. We find that the effectiveness of general and emergency medical services depends on the requirements of healthcare resources such as beds and equipment in times of pandemics. By contrast, the capacity of individual BMW treatment processes such as incineration, autoclave, shredder, and deep burial would have a bearing on the efficiency of both BMW management and the total system for greener operations. In the second phase, we evaluated the impact of context-specific exogenous factors using a regression model. We found that literacy level was the most important factor at all three parallel stages separately. Additionally, the total population and number of female vaccinated persons were significant across the system while population density and number of male vaccinated persons were inconsequential factors. Interestingly, the Wilcoxon rank-sum hypothesis test offered no evidence to suggest that performance of general care and BMW management, and emergency care and BMW management were equivalent. Implications of these findings for academic theory, practitioners, and policy makers are offered. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is an important managerial tool for evaluating the performance of decision-making units (DMUs). The conventional DEA models are mostly in the static environment using deterministic/crisp data for input and output parameters. However, in real situations, input and output data cannot always be obtained accurately because of vagueness due to fluctuating market conditions. Such vagueness in input and output data can be tackled with fuzzy numbers. So, the aim of the current study is to extend the crisp DEA into fuzzy DEA (FDEA). In this study, the input and output data are considered as fuzzy numbers (FNs), particularly the triangular fuzzy numbers (TFNs). Then, a multi-objective approach is developed to solve the FDEA model to measure the performance efficiencies of DMUs. Further, the DMUs are ranked according to their efficiencies obtained. Finally, the developed FMODEA model and rankings of DMUs are illustrated with an application on real data 13 educational institutions in India. [ABSTRACT FROM AUTHOR]

Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. • Data Envelopment analysis was estimated to derive the paddy efficiency among the tribal villages of eastern India. • The DEA estimates show that average Technical efficiency in the study region is much below the national average. • Irrigated farmers are more efficient than the rain-fed farmers. Surface irrigation is the dominate factor. • Rain-fed paddy cultivators' farm efficiency would improve by 6.5%, if they had irrigated. • Semi-mountainous regions with little access to surface irrigation must explore micro-irrigation practices such as drips and sprinklers. [ABSTRACT FROM AUTHOR]

Indian airline industry has been facing the financial and operational challenges for the last many years. The challenges have increased many folds due to the ongoing pandemic. Many of the airlines are struggling for their survival, and therefore, there is an utmost need of redesigning the business model for this industry. The objective of this study is to define a two-stage data envelopment analysis (DEA) model for the airline industry to promote a smooth coordination between its two internal operational stages. The proposed two-stage DEA model is among the foremost and eloquent effort in evaluating the performance of two stages. Stages I and II are defined in accordance with the working process of airlines. Efficiency of Stage I is called the structural efficiency, while that of Stage II is called an operational efficiency. The proposed model is defined with three types of inputs, one intermediate and two types of output variables. The model has been illustrated for eleven airlines operating in India for the year 2017–18. This study will help the decision makers in assessing the airline's performance with an identification of factors that are responsible for their poor performance. The study also suggests the percentage of operating cost that can be optimally shared between two stages. [ABSTRACT FROM AUTHOR]

This paper measures the environmental efficiency of the grossly polluting Indian leather industry which faces stringent environmental regulations. The environmental efficiency measure accounts for associated bad outputs (total suspended solids and chromium) with the good output (leather products) of firms and hence, provides an important benchmark for improving environmental performance. Drawing data from fieldwork in the Kanpur industry of India, the study estimates efficiency using the directional distance function approach under three directional vectors. The results reveal that the efficiency of firms is underestimated when bad outputs are omitted in the production technology. There is significant potential to increase leather production and reduce pollutants across firms. The study confirms that regulation improves the environmental efficiency of leather firms. However, regulation imposes an opportunity cost on firms of an average 3% loss in expanding leather output and reducing inputs. The study recommends mandating the use of cleaner technology and market-based instruments to improve environmental efficiency. [ABSTRACT FROM AUTHOR]

This study set out to measure the performance of organic farming in India. The study utilized data of 21 Indian states for the period 2016–2017, and the states are considered as decision making units for performance assessment. In the first step, we compared the "pure organic production" or "production technology set A" with the "production including the conversion period" of organic farming or "production technology set B" by incorporating the Data Envelopment Analysis (DEA) method of performance evaluation. In the second step, we estimated the performance of "pure organic production" and applied the bootstrap DEA method to correct the biasness in the traditional DEA approach. The input‐oriented DEA model reveals that on average technical efficiency of organic farming production is 84.7%, and the output‐oriented DEA model concludes 20% technical efficiency which means organic farming production can be increased by 80% with the same amount of inputs utilization. Evidence is presented in the conclusion which shows that organic farming practice is highly beneficial for land degraded states of India. The findings can contribute to implement policies for organic farming production in Indian states. [ABSTRACT FROM AUTHOR]

The issue of public expenditure efficiency has drawn the attention of both policymakers and researchers globally. The paper attempts to measure the efficiency of government expenditures on the social sector, especially health and education, among the major Indian states using various data envelopment analysis. The paper also attempts to understand what drives public expenditure efficiency. The results suggest that there is a wide variation in the efficiency of public spending as well as scope for resource‐saving at the subnational level in India. It finds that states are spending their resources more efficiently on education than on health and the overall social sector spending. It also finds that quality of governance, economic growth, and mothers' schooling affects the efficiency of education, health, and social sector with governance to have a larger effect compared to other factors. Overall, the study suggests that improving governance could yield better outcomes from public expenditure. Highlights: The paper looks at understanding the issue of public expenditure of efficiency of education, health, and overall social sector over the 15 year period since the year 2000, broadly covering the millennium development goal period at the subnational level in India.By using data envelopment analysis, the study derives the efficiency scores for major states for three‐time periods for input‐oriented, output‐oriented, and nonoriented conditions.The results broadly suggest that there is wide divergence in terms of efficiency levels across the states and also finds some spatial patterns. States in Western region appear to be more efficient than compared to other regions.Overall, the states appear to be spending efficiently on the education compared to health.In terms of what explains such divergences in efficiency levels, the study looks at broader determinants‐overall governance‐with conditional variables such as level of incomes and mothers' schooling.The econometric results suggest that it is the level of governance that affects the efficiency levels at the subnational level in India. [ABSTRACT FROM AUTHOR]

The objective of this study is to estimate technical efficiency of farm machinery manufacturers in Central India. The statistical test for the presence of technical inefficiency has been performed using stochastic frontier production model. Data envelopment analysis (DEA) has been used to identify existing returns to scale in farm machinery manufacturing units. The slacks-based DEA has been used to estimate input excess and output shortfall in the manufacturing system. Results indicate that out of the total variation, 69% was due to technical inefficiency in the manufacturing system, whereas 31% was due to stochastic errors. The estimated radius of stability was varied from 0 to 1.74 and the classification (efficient and inefficient manufacturers) was found robust against data alteration within the estimated radius of stability. The results showed that a manufacturer has to increase annual turnover by INR 40.7 million to become efficient. [ABSTRACT FROM AUTHOR]

Twenty-six new data envelopment analysis (DEA) models with 55 biohydrogen production experiments categorized into three groups including dark fermentation (DF), photo fermentation (PF), and dark-photo sequential fermentation (DF-PF) technologies, are used to evaluate their biohydrogen yield efficiency. The results reveal the average yield efficiencies of DF, PF and DF-PF are 0.2844, 0.3460 and 0.7040, respectively. The most efficient overall combination of biohydrogen inputs is PhBR1/Rhodobacter capsulatus B10/Rhodobacter capsulatus in DF-PF. Statistical tests demonstrate DF-PF has statistically double the efficiency of PF and DF, and the efficiency of PF significantly exceeds that of DF, supporting some of the literature findings. A flexible DEA model must be carefully chosen when evaluating biohydrogen production. All inputs and outputs of biohydrogen statistically influenced yield efficiency to a significant level. India and Japan are the top two economies benefitting from improved biohydrogen yield efficiency. Improving biohydrogen yield efficiency can improve macroeconomic growth and develop the renewable hydrogen and biohydrogen industry. • New DEA models are used to evaluate efficiency of bioH2 fermentation. • Average efficiencies of dark, photo and dark-photo fermentation are obtained. • PhBR1/Rhodobacter capsulatus B10/Rhodobacter capsulatus is most efficient inputs. • Input and output variables statistically influence bioH2 production efficiency. • India will benefit most from improved efficiency, increasing bioH2 output by 19.31%. [ABSTRACT FROM AUTHOR]

The prime objective of this article is to construct a robust macroeconomic performance (MEP) index of the Indian economy using the data envelopment analysis (DEA) approach. Seven major macro indicators, namely, economic growth, employment rate, terms of trade, inflation rate, fiscal deficit, pollution, and climate change are used to compute MEP and Eco‐MEP indices of the Indian economy from 1980–1981 to 2018–2019. Overall, both the MEP and Eco‐MEP index scores have quite similar best performing years worst performing years, and have also captured the major events that affected adversely the Indian economy during the past decades. The trend in the overall performance of the Indian economy was better in the 1980s and the 1990s but has deteriorated since 2000. The autoregressive distributed lag bounds testing approaches to cointegration methods are used to test the robustness/utility of these indices. The estimated results show that MEP and Eco‐MEP have a positive impact on private investment, foreign investment inflows, foreign direct investment, and a negative effect on the current account deficit. Hence, the suggested composite MEP index is stable, robust and truly captures the economic performance of India. The constructed MEP composite index may be used by foreign investors, rating agencies, private investors, and policymakers for their planning and decision‐making processes. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is a winter cereal crop with higher energy footprints. We therefore aimed to budget energy use, establish input-output relationships, and to perform data envelopment analysis (DEA) based optimization in wheat established with innovative super seeder technology. Data-set of 45 decision-making units (DMUs) collected during face-to-face interviews were changed to energy units for enhancing their energy efficiency. These results revealed that total energy input (E I) of 22.6 GJ ha−1 and output energy of 125.3 GJ ha−1 yielded energy productivity of 0.231 kg MJ−1. The net energy gain (102.7 GJ ha−1) accomplished with an energy ratio of 5.6 indicates that super seeder technology was energy-efficient. Direct and indirect energy share ∼53.2 and 46.8%, whilst renewable and non-renewable energy comprised ∼24.8 and 75.2% of E I , respectively. Fertilizer-N emerged as the major energy input (∼44.7% of E I), followed by irrigation water (∼17.4%) and electricity (15.1%). The DEA based benchmarking elucidated 23 DMUs (∼51.1% of total studied) as efficient, whilst others inefficient. An average technical efficiency of 0.89 illustrates ∼11% E I saving possibilities by the adoption of best management practices. These results underpin the overwhelming significance of efficient fertilizer-N, irrigation water and electricity for reduced energy footprints in north-western India. [Display omitted] • Energy use efficiency of wheat established with super seeder technology was studied. • Net energy gain of 102.7 ± 1.7 GJ ha−1 was achieved with energy ratio (ER) of 5.6. • Fertilizer-N has highest energy input, comprised ∼44.7% of total energy input. • Data envelopment analysis (DEA) elucidated ∼51.1% wheat farms as energy efficient. • About 11% (2046.7 MJ ha−1) energy could be saved following recommendations of DEA. [ABSTRACT FROM AUTHOR]

In the current study, flood risk assessment of densely populated coastal urban Surat City, on the bank of the lower Tapi River in India, was conducted by combining the hydrodynamic model-based flood hazard and often neglected socioeconomic vulnerability. A two-dimensional (2D) hydrodynamic (HD) model was developed using physically surveyed topographic data and the existing land use land cover (LULC) of the study area (5248 km2). The satisfactory performance of the developed model was ascertained by comparing the observed and simulated water levels/depths across the river and floodplain. The 2D HD model outputs with geographic information system (GIS) applications were further used to develop probabilistic multiparameter flood hazard maps for coastal urban city. During a 100-year return period flood (Peak discharge = 34,459 m3/s), 86.5% of Surat City and its outskirt area was submerged, with 37% under the high hazard category. The north and west zones are the worst affected areas in Surat City. The socioeconomic sensitivity and adaptive capacity indicators were selected at the city's lowest administrative (ward) level. The socioeconomic vulnerability was evaluated by employing the robust data envelopment analysis (DEA) technique. Fifty-five of 89 wards in Surat City, covering 60% of the area under the jurisdiction of the Municipal Corporation, are highly vulnerable. Finally, the flood risk assessment of the city was conducted using a bivariate technique describing the distinctive contribution of flood hazard and socioeconomic vulnerability to risk. The wards adjoining the river and creek are at high flood risk, with an equal contribution of hazard and vulnerability. The ward-level hazard, vulnerability, and risk assessment of the city will help local and disaster management authorities to priorities high risk areas while planning flood management and mitigation strategies. [Display omitted] • A 2D HD model has been developed for the lower Tapi basin, India, and its performance is evaluated from past flood events. • Probabilistic multiparameter flood hazard obtained through 2D HD model output and GIS application. • Ward-level socioeconomic vulnerability is computed using census-based indicators and the robust DEA technique. • A bivariate risk technique is used to develop flood risk maps by integrating flood hazard and socioeconomic vulnerability. • Developed flood risk maps helpful in identifying and prioritizing high risk areas. [ABSTRACT FROM AUTHOR]

Analyzing engineering colleges in India with Data Envelopment Analysis (DEA) is the focus of this study. Engineering colleges pioneer in teaching and research, creating two sub-processes within engineering education. Thus, our study considers each engineering college to comprise two parallel non-homogenous sub-processes, such as teaching and researching. Unlike the conventional DEA models, where the system is treated as a black box, this work attempts to independently assess the impact of each parallel sub-process so that it is possible to identify specific changes in the respective processes to improve the overall efficiency. In particular, our study analyzed the efficiency of engineering colleges based on the National Institute Ranking Framework (NIRF) data. Further, the comparison of the efficiency scores from teaching and research indicates that, on average, the institutes are more efficient in teaching than researching. The current study helps policymakers set priorities and course corrections for performance improvement programs of engineering colleges in India. • We consider parallel non-homogeneous sub-processes in Data Envelopment Analysis (DEA) model. • We proposed the variable return to scale model for parallel non-homogeneous sub-processes in DEA. • We applied the above methodology to engineering colleges in India, with teaching and research being sub-processes. • We found that engineering colleges are more efficient in teaching than research. • With a ranking system like the NIRF, colleges have improved their efficiency. [ABSTRACT FROM AUTHOR]

This paper analyses the performance of non-banking finance companies (NBFC) in the Indian context using data envelopment analysis (DEA). The underlying objective of this study is to fill the void in the domain of NBFC, although a lot of research has been done on the banking industry in the context of the application of DEA, but none on NBFCs. The paper takes the panel data of the last 5 years (2014–2018) to calculate super-efficiencies in the first stage and then regresses the same on exogenous factors in stage-2. Descriptive statistics are used to estimate the efficiency by carrying out the calculations using both the traditional models (OTE, PTE and SE) and super-efficiency model. A comparison is made by categorizing NBFC's based on the size of total assets and using non-parametric statistic tests to find whether the efficiency scores are significantly different across different categories. The second stage DEA analysis uses Tobit regression to find the exogenous factors which affect the model significantly. Based on traditional models, the total number of efficient DMUs are 8 out of 43 while there are 15 after considering the super-efficiency algorithm. Malmquist Indices are used to study the productivity indices of NBFCs over the last 5 years, and it gives us a maximum productivity growth of 8.53%. It was noticed that there is a significant difference in the mean efficiency values of different sized NBFC's which can be explained by the lack of standardization in the NBFC domain and the few companies which are listed on the stock market. The managers should not consider ROE as a significant indicator of efficiency and should instead focus on aspects such as ROA and income diversity. Through the Malmquist analysis, the managers can break down the productivity change into technical and efficiency shifts for further investigation. [ABSTRACT FROM AUTHOR]

Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the performance of major public road transport organizations in India. The contribution of the paper lies in integrating four multicriteria decision-making (MCDM) techniques to assess the relative performance of public road transportation systems on twenty-three criteria across two consecutive years. The paper classifies the criteria into functional heads and establishes the relative importance of heads using the analytical hierarchical process (AHP). The efficiency scores of each organization referred to as a decision-making unit (DMU) were computed for the classified heads using the Data Envelopment Analysis (DEA) approach. The multicriteria optimization and compromise solution technique "VlseKriterijumska Optimizacija I Kompromisno Resenje" (VIKOR) was used to assign a final rank to each of the DMUs using computed efficiency scores and established weights. Finally, we analyzed the performance of the DMUs across the two consecutive years using the Malmquist Productivity Index (MPI). Our key findings are as follows: First, the performance of all DMUs has improved in the second year with respect to the first year; second, significant improvement is observed in the "expenses" functional head which carries a substantial weight among the functional heads; third, barring few DMUs, the performance of the majority of DMUs has worsened in the "accident" functional head; fourth, while few DMUs have been consistently very good performers in both the years, there are also few DMUs which have consistently performed poorly in both the years. The inferences drawn from the study can be leveraged for future policy formulations by the state government and local municipal corporations and for sharing best practices among the DMUs. [ABSTRACT FROM AUTHOR]

This study combines a data envelopment analysis, a dynamic computable general equilibrium model with estimated secondary material flows for circular economy, based on economist Joseph Schumpeter's macroeconomic theory, to develop a novel soft-link model to determine the efficiency of forty-three dark-fermentative technology of biohydrogen, and technology improvement impacts on biohydrogen output and supply price for six major emerging Asian countries. The integrated model is found to be feasible. This study finds that efficiency of continuous technology significantly exceeds that of batch technology. Biomass substrate concentration is the most important input in the generation of biohydrogen statistically; pH influences the efficiency of the batch technology, and the efficiency of continuous production technologies significantly exceeds that of batch technologies, but still have a gap to improve to full production efficiency for most of continuous technologies. India and China generate highest output growth of biohydrogen in baseline scenario. Japan and India can most benefit from improvements in batch and continuous biohydrogen production technology. The models and results of this study provides guidelines and references for decision-makers in industry and government who are responsible for reforming future energy policy. • A soft-link DEA-CGE model was developed, and its effectiveness confirmed. • Efficiency and improvement potential of dark fermentation technologies are provided. • Efficiency of continuous technology significantly exceeds that of batch technology. • Biomass substrate concentration is the most important input in generation of bioH2. • Japan and India are the countries that benefit most from improved bioH2 technology. [ABSTRACT FROM AUTHOR]

This paper analyzes efficiency and productivity changes in Emergency Obstetric and Newborn care (EmONC) in regional public hospitals in India. A novel approach of Weighted Russell Directional Distance Function Model (WRDDFM) has been employed. This model considers all the radial and non-radial slacks associated with good outputs as well as the undesirable outcomes while estimating inefficiency and helps to identify the source of the inefficiency. The empirical results based on a sample of 46 regional public hospitals from 2006–07 to 2013–14 show on average overall inefficiency of 24% and much of these are due to inefficiencies associated with undesirable outcome of stillbirths and intra-uterine deaths. Further, on an average the sampled hospitals have experienced marginal productivity decline over the reform period. The study indicates that there is a greater scope for efficiency and productivity gains in public hospitals, particularly if the undesirable outcome of still birth is minimized. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is highly energy intensive winter crop grown in rice-wheat and cotton-wheat cropping systems in north-western India. In this study, energy input-output relationships for wheat cultivation were estimated from data collected from 80 wheat producers (40 each in rice-wheat and cotton-wheat cropping system) by face-to-face survey. Energy input through irrigation water was significantly (p < 0.05) higher by 517 MJ ha-1 (14.5%) in wheat under cotton-wheat (4093 ± 133 MJ ha-1), compared with rice-wheat (3576 ± 124 MJ ha-1); while energy input difference from other sources was non-significant among cropping systems. Data pooled for cropping systems revealed that chemical fertilizers comprised ∼45%, while irrigation, electricity and diesel fuel consumption each shared ∼15% towards total input energy (E I). Wheat cultivation was energy efficient with E I of 25.6 ± 0.5 GJ ha-1 and total output energy (E O) of 191.7 ± 3.6 GJ ha-1 produced net energy gain (NEG) of 166.1 ± 3.5 GJ ha-1. Data Envelopment Analysis (DEA) elucidated 28 decision making units (DMUs) as energy efficient with technical score (TE) of 1.00, while 52 DMUs (∼65% of total DMUs) with TE < 1.00 were inefficient. Therefore, by the adoption of recommendations of the study, 3819 ± 641 MJ ha-1 (∼12% of E I) energy saving is possible from different inputs in wheat cultivation. • Energy input was 26 GJ ha-1 and energy output was 192 GJ ha-1 in wheat cultivation. • Net energy gain of 166 GJ ha-1 shows that wheat cultivation is energy efficient. • Data envelopment analysis was used to optimize input energy in wheat cultivation. • Technical efficiency of 52 wheat farms (65% of total) was <1.00 • 12% (3819 MJ ha-1) of total input energy could be saved under optimized conditions. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) is an important energy intensive cereal crop in India. In the present study, energy input and output were audited to optimize energy use in rice cultivation using data collected from 40 farmers during face-to-face interview. Average total energy input (E I) of 52.4 ± 1.3 GJ ha−1 produced total energy output (E O) of 259 ± 4.7 GJ ha−1, with energy productivity (E P) of 0.16 kg MJ−1. Results revealed that rice cultivation was energy efficient with energy ratio (E R) of 5.0 ± 0.12 and net energy gain (NEG) of 206 ± 4.5 GJ ha−1. In rice, irrigation water (∼40%) and chemical fertilizers (24.7%) had highest contribution towards E I. A non-parametric production function of DEA (Data Envelopment Analysis) applied to optimize energy use in rice, elucidated 20 decision making units (DMUs) as efficient, whilst 50% as inefficient. The average technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) of rice farms were 0.94, 0.97 and 0.97, respectively. Our results revealed that between 9.4 and 2170 MJ ha−1 energy (0.2 and 46.9% of total saving) could be saved, if inefficient DMUs adopt the management practices of efficient DMUs. An average energy saving of 579 ± 166 MJ ha−1 by efficient DMUs accounts for ∼8.8% of the E I. • Energy input was 52.4 GJ ha−1 and energy output was 259 GJ ha−1 in rice cultivation. • Irrigation and fertilizers had highest contribution (64.7%) in total energy input. • Data envelopment analysis was used to optimize input energy use in rice cultivation. • Saving of 8.8% of total energy input is possible with current yield level. [ABSTRACT FROM AUTHOR]

Abstract This study aims at to make a heuristic analysis of energy efficiency performance of Indian paper industry in a non-parametric production theoretic approach. Using the data for 2004–05 to 2013–14, radial and non-radial variants of DEA were employed to estimate energy saving potential and identify the relative position of paper industry across the states. Preliminary analysis in terms of energy intensity scores showed declining trends of energy intensity over the period of analysis for most of the states except states like Assam, Chhattisgarh, Kerala and Punjab where it does not decline much. Assuming a variable returns to scale (VRS) technology under the conditions of inherent heterogeneity and market imperfection, the slack-based measure (SBM) reported a much higher energy saving potential than reported by other measures in the study. Further by taking scale efficiency into account, the study documented the higher contribution of pure energy inefficiency in explaining the total energy inefficiency within the paper industry relative to scale inefficiency. Finally, the study recommends the propagation of energy efficiency program through a market-based and regulatory mechanism along with consolidation and technological advancement of individual units, in order to tap vast energy saving potential and thereby ensure the growth and environmental sustainability. Highlights • The paper examined the energy efficiency performance of Indian paper industry. • Both radial (BCC) and the non-radial (SBM) measures of energy efficiency, were applied. • BCC measure reported an energy saving potential of 16%. • SBM measure reported an energy saving potential of 24%, higher than BCC measure. • Scale efficiency implied the relatively higher contribution of pure energy inefficiency. [ABSTRACT FROM AUTHOR]

Conventional Data Envelopment Analysis (DEA) models focus only on initial inputs and final outputs for efficiency evaluation. Thus, these models treat the production process as a ‘black box’, i.e., they do not take into account how exactly inputs are related to outputs. Various models that came later take care of internal processes of DMU. The existing models of internal processes for parallel sub-units consist of three stages: the first stage calculates the relative weights of sub-units, the second stage calculates the efficiencies of sub-units, and in the third stage efficiencies of sub-units are aggregated as the efficiency of DMU. It is observed that when existing models of internal processes are applied to non-homogeneous parallel sub-units, in the first stage, the weight assigned to the maximum efficient sub-unit is one and to other sub-units is zero. This implies that the efficiency of a DMU is equal to the maximum of efficiencies of its sub-units indicating that the efficiency of a DMU is not sensitive to the efficiencies of sub-units other than the sub-unit with maximum efficiency. This paper proposes a single stage DEA approach where the efficiency of a DMU and its sub-units can be measured simultaneously. The advantage of the proposed approach is that the efficiency of a DMU is sensitive to the changes in the efficiency of its sub-units, and weights of sub-units can be assigned a priori by the decision maker. The development of the proposed approach is inspired from the growing interest in evaluating efficiency of higher education system in India. In the proposed application, states are considered as DMUs and universities, colleges and stand-alone institutions are taken as three non-homogeneous parallel sub-units of DMUs. [ABSTRACT FROM AUTHOR]

This study applies data envelopment analysis and the stochastic frontier approach to a sample of Indian commercial banks to discuss the inconsistencies between these models. We find that DEA average efficiency scores are, in general, lower than those from the SFA model. However, both models indicate similar trends on efficiency scores over the years, which we state is more relevant than efficiency levels themselves. We also find that the rank correlation between these efficiency scores is low. This means that the application of only one frontier model may yield misleading conclusions. We point out that a thorough consideration of the suitability of a deterministic or a parametric frontier to the framework in analysis should guide the choice between the application of parametric or non-parametric methodologies. [ABSTRACT FROM AUTHOR]

State Transport Undertakings (STUs) are key players in providing mass road transport in India. Given that they operate under high levels of government imposed regulatory constraints, it is imperative to study their efficiency levels. Given that capital is a relatively scarce resource in developing countries like India, it is important to obtain efficiency in the short-run where some inputs are fixed as well as over the long run, where all inputs are variable. The technique used for capturing efficiency is Data Envelopment Analysis (DEA). A key possible limitation of DEA models based on physical inputs and outputs is that for an inefficient firm, reduction in some or all inputs may be recommended. It may often be desirable for an inefficient firm to increase some less expensive inputs while reducing the use of relatively expensive ones. Hence, when market price data is available, it is advisable to use the cost variant of DEA. Also, it is possible to determine variable cost efficiency in the short run when some inputs cannot be varied. Such inputs are referred to as “quasi-fixed” inputs. In this paper, we examine short and long term efficiencies of select bus companies in India known as State Transport Undertakings (STUs) over a period of 10 years. Fleet strength has been used as the quasi-fixed input. It is possible to ascertain, through a comparison of shadow price of the quasi-fixed input, vis-à-vis its market price, as to whether the quantity of this input is sub-optimally small or large. It is found that by adopting efficiency enhancing practices, STUs can cumulatively reduce their operating costs to the extent of 9123.35 million dollars. Also the tendency to minimize costs is found to be declining over time. In the short run some STUs are found to operate with a sub optimally low fleet size. [ABSTRACT FROM AUTHOR]

Owing to the importance of internal structure of decision making units (DMUs) and data uncertainties in real situations, the present paper focuses on multi-component data envelopment analysis (MC-DEA) approach with imprecise data. The undesirable outputs and shared resources are also incorporated in the production process of multi-component DMUs to validate real problems. The interval efficiencies of DMUs and their components in MC-DEA are often challenging with imprecise data. In many practical situations, different set of weights may be resulted into valid efficiency intervals for DMUs but invalid interval efficiencies for their components. Therefore, the present study proposes a new common set of weights methodology, based on interval arithmetic and unified production frontier, to determine unique weights for measuring these interval efficiencies. It is a two-level mathematical programming approach that preserves linearity of DEA and exhibits stronger discrimination power among the DMUs as compared to some existing approaches. Theoretically, the aggregate efficiency interval of each DMU lies between the components' interval efficiencies. Further, the proposed approach is also applied to banks in India for proving its acceptability in practical applications. The performance of each bank is investigated in terms of two components: general business and bancassurance business for the years 2011-2013. The present study emphasized expanding pattern of bancassurance business in current market scenario with more percentage increase as contrasted to general business. [ABSTRACT FROM AUTHOR]

In the new economy, strategic alliances enable business to gain competitive advantages through access to partner's resources, including markets, technologies, capital and people. In the context of the provision of the Electricity Act in 2003 has allowed a dynamic competitive growth of public sector and private sector in Indian electricity market, it becomes critical for Indian electricity firms to enter strategic alliance to improve business performance and competitive advantages by seeking right partners that augment strengths or ameliorate weaknesses. Over the 5-year time frame in the past to have 4-year forecasting, this paper tries to suggest a new approach of integrated GM (1,1), neural network and DEA model in order to assist Calcutta Electric Supply Corporation in selecting its right strategic alliance partner. The results indicate that Power Grid Corporation and Reliance Infrastructure are the very best candidates for Calcutta Electric Supply Corporation to form strategic alliances. The findings are not only good for the target company but also beneficial for the partners. Furthermore, the proposed method allows upper management to properly adjust input and output factors for industry, based on their calculations for reference purposes. [ABSTRACT FROM AUTHOR]

By using the directional distance function (DDF) of data envelopment analysis (DEA), this study measures the technical efficiency of 37 Indian state road transport undertakings (SRTUs) for the year 2012–13. We employ the DDF as a tool for analyzing a joint production function with both desirable and undesirable outputs (i.e., the number of accidents). A comparison between the results with and without accidents shows that several SRTUs have experienced significant changes in their efficiency scores as well as in their rankings after accounting for the undesirable output. This indicates the importance of including the number of accidents – a safety standard – as representative of the undesirable output in computing the efficiency scores of SRTUs. The results of the Tobit model indicate that SRTUs with greater vehicle productivity are more efficient under both conventional DEA and DDF approaches. We also employed zero-truncated negative binomial model to assess the factors influencing the number of road accident experienced by the Indian SRTUs and found that the accident count was significantly influenced by fleet utilization and vehicle productivity. [ABSTRACT FROM AUTHOR]

With a continuously growing population, India faces a real challenge in fulfilling the target of food security for all of her citizen. Parallel to increment of food production, better processing of it as well plays a vital role in ensuring nutrient food to all in a sustainable fashion. Food processing industry may plays a vital role in this direction and, therefore, occupies an important p lace in India's overall economic growth, in general and that of her manufacturing sector, in particular. For its significant share to the industrial output and both direct and indirect employment generating capacity, food processing industry is considered to be a remarkable pooling factor for the prosperity of agricultural sector as well. In this study, we explore the performance of the production units of this industry. We have analyzed data on Annual Financial Statements of food processing companies, collected through Prowess database of Centre for Monitoring Indian Economy (CMIE) during 2000-2015. We use a two stage methodology for our analyses. Using data envelopment analysis (DEA) method in the first stage, we obtain technical efficiency (TE) score of the individual production units of the industry. In this connection, we also examine the extent of diverse technologies used across its various subsectors, through a measure called technology closeness ratio (TCR), which we explain in the paper in details. We then explain such TE scores in terms of certain company -specific variables using simple ordinary least squares (OLS) regression method, to have an idea on the factors that may influence such performance. Results suggest that the units producing dairy products and sugar recorded relatively lower level of TE, whereas those producing vegetable oil and products are more technically efficient as compared to other sub-sectors within the industry. Overall TCR varies from 0.63 (for sugar and dairy products) to 0.93 (for vegetable oil and products), implying thereby that the former two have an opportunity to improve their overall performance to a sizeable extent through technological upgradation, whereas the latter does not have much scope to improve themselves in this regard, and can improve their overall performance only through more efficient utilization of available resources! Moreover, observed evidence of positive impact of R and D intensity and infrastructural factor on performance have a remarkable implication on the role of the government for providing necessary infrastructural facilities and (probably) initiating innovative scheme(s) to make research and development activities more attractive to them!. [ABSTRACT FROM AUTHOR]

Indian banking sector is characterized with intense competition, amongst the best regulated in the world and rapid growth. Indian banking sector have undergone rapid reforms and liberalization in last two decades, which resulted into advent of new and strong private sector banks, increased technology adoptions, rapid branch expansion, prudential regulations, focus on financial inclusion, market discipline of public sector banks and demanding customers. With Indian Banking system poised to become the world’s 3rd largest by 2025 and owing to the dynamism in banking sector the paper examines the bank efficiency. Most past studies focused on the impact of some or other regulatory phase, some segments of banking and under constant return scale assumption. Our study is distinct to the extent that we examine the bank efficiency after the Phase III and IV reforms and subprime crisis, across all ownership segments of Scheduled Commercial Banks and under plausible assumption of variable returns of scale given the dynamic banking environment. We employ non-parametric Data Envelopment Analysis using intermediation approach under of variable return of scale. Our study period consist of 2008-2012 post reform and subprime crisis period and covers 26 public, 19 private and 21 foreign banks operating in India. We further examine the variation in efficiency across different ownership groups and small and large bank sizes over the period. Our findings indicate that Public sector banks’ average efficiency scores were better than that of private sector while foreign banks had the lowest efficiency score. Private sector banks had lower heterogeneity in efficiency compared to public sector banks. The smaller banks showed better efficiency compared to larger banks across the ownership groups. We find that the banking reforms have seen improvement in efficiency of public sector banks as compared to private and foreign banks. Thus our findings differ from studies on other emerging markets indicating efficiency improvement of foreign banks as compared to pubic banks. While banks have shown overall efficiency the lower technical efficiency indicate inefficiency in large banks probably due to sub optimal scale and scope efficiency. Our study point outs management of assets, branch rationalization and use of technology to improve the bank efficiency. The study suggests policy focus on reduction of non-performing assets and rationalization of staff and branches to obtain efficiency gain. Regulatory reforms in Indian Banking sector saw improvement in efficiency of public and private banks and efficiency scores differed across various ownership and large and small bank groups. [ABSTRACT FROM AUTHOR]

This study focuses on the structural efficiency of 42 Indian regional airports over five years from 2014 to 2019. A two-stage data envelopment analysis (DEA) was applied to assess the structural efficiencies of airports. We find that distance to the closest airport, distance from the city centre, per capita income, accessibility and profitability of the airport significantly impact the structural efficiency scores. We also calculate the productivity growth of the sample airports over five years using the Malmquist Productivity Index. Airport productivity rankings and sources of inefficiency provide airport authorities and policymakers with valuable insights into the potential for improvement. • Five regional airports were found to be structurally efficient from 2014 to 2019. • Most of the regional airports have increasing returns to scale in all the years. • There is an efficiency gap of 30% between international and domestic airports in all the years. • Airport competition works differently in regional airports. • Airport productivity is influenced more by the airport's capacity to utilize its resources than by external factors. [ABSTRACT FROM AUTHOR]

The importance of efficiency in resource utilization in healthcare sector has been recognized globally. In this paper we focus on efficiency of healthcare system at sub-state level (i.e., district level) in India using Gujarat state and its district level data for 2012-13. In spite of being an economically advanced state, in terms of infant mortality rate (IMR) the state is not the lowest. We explore the reasons for relative performance of different districts with data envelopment analysis (DEA). We used IMR as output variables. Using principal component analysis we tried a sub-set of variables, which had low correlations. Thus, four factor scores relating to medical officer, lady medical officer, Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homoeopathy doctor, pharmacist, were used for DEA. We have focused on Charnes, Cooper, and Rhodes scores (or constant returns to scale technical efficiency score), and discussed efficiency rankings based on these. Thus, our results pertaining to district level health system efficiency in Gujarat State indicate that some of the districts have low efficiency in utilization of inputs like doctors, beds and workload per health institutions. There are also other districts, which need more of these inputs, which may enhance their output and efficiency. Thus, it is suggested that the efficiency in Valsad needs an improvement much more than other districts, whereas districts like Ahmadabad and Surat need more of both medical manpower and facilities. Even in case of Vadodara and Rajkot, the ranking in terms of most of medical manpower and facilities is low and thus these districts may also be benefitted by additional inputs. Hence, there is a mix of both inefficiency and inadequacy of inputs, which is reflected in our results. [ABSTRACT FROM AUTHOR]

Background: The evaluation of primary healthcare services provided by health training centers of a private medical college has not been studied in comparison with government health facilities in Indian context. Data envelopment analysis (DEA) is one such technique of operations research, which can be used on health facilities for identifying efficient operating practices and strategies for relatively efficient or inefficient health centers by calculating their efficiency scores. Materials and Methods: This study was carried out by DEA technique by using basic radial models (constant ratio to scale (CRS)) in linear programming via DEAOS free online Software among four decision making units (DMUs; by comparing efficiency of two private health centers of a private medical college of India with two public health centers) in district Muzaffarnagar of state Uttar Pradesh. The input and output records of all these health facilities (two from private and two from Government); for 6 months duration from 1st Jan 2014 to 1st July 2014 was taken for deciding their efficiency scores. Results: The efficiency scores of primary healthcare services in presence of doctors (100 vs 30%) and presence of health staff (100 vs 92%) were significantly better from government health facilities as compared to private health facilities (P < 0.0001). Conclusions: The evaluation of primary healthcare services delivery by DEA technique reveals that the government health facilities group were more efficient in delivery of primary healthcare services as compared to private training health facilities group, which can be further clarified in by more in-depth studies in future. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy set (IFS) is an extension of fuzzy set and an approach to define a fuzzy set where available information is not sufficient to define an imprecise concept by means of a conventional fuzzy set. The existing fuzzy DEA (FDEA) models for measuring relative fuzzy efficiencies of decision making units (DMUs) are limited to fuzzy input/output data. However, in real life applications, some inputs and outputs of subjective, linguistic and vague forms may possess intuitionistic fuzzy essence instead of fuzziness. Therefore, in the present study, we extend FDEA to intuitionistic fuzzy DEA (IFDEA) in which the input/output data are represented by intuitionistic fuzzy numbers (IFNs), in particular triangular IFNs (TIFNs). This is the first study in analysing optimistic and pessimistic efficiencies with intuitionistic fuzzy input/output data in DEA. In this study, we develop models to measure optimistic and pessimistic efficiencies of each DMU in intuitionistic fuzzy environment (IFE). By using super-efficiency technique, we develop algorithms to obtain the complete ranking of the DMUs when optimistic and pessimistic situations are considered separately. Further, to rank the DMUs when both optimistic and pessimistic situations are taken simultaneously as hybrid approach, we propose two alternate ranking methods based on levels of inefficiencies and efficiencies respectively. To address the overall performance using optimistic and pessimistic situations together in IFEs, we propose a hybrid IFDEA performance decision model. To validate the proposed methodology and proposed ranking methods, we illustrate different numerical examples and then compare the results with an existing ranking approach based on geometric average efficiency index. Moreover, we present an application of the proposed approach to the banking sector in which two inputs, namely, labour and operating expenses possess intuitionistic fuzzy essence at branch level, and are represented as TIFNs. [ABSTRACT FROM AUTHOR]

Technical progress is one of the best strategies to meet the dual challenges of economic growth and carbon emissions control in developing countries such as India. In this research, an empirical study of India's carbon emissions was conducted to understand the effects of various technical factors on total and industrial carbon emission intensity. Ten driving factors of carbon emission in 56 sectors of India are decomposed by combining Index Decomposition Analysis (IDA) and Production-theoretical Decomposition Analysis (PDA). The results reveal that (i) the pure carbon-reduction technical efficiency and the scale efficiency of carbon-reduction technology in India have not improved, and the technical progress of pure carbon-reduction and the scale efficiency of carbon-reduction technology has played a positive role in carbon emission. (ii) The scale efficiency of pure energy-saving technology progress is the most important factor in restraining carbon emissions, and pure energy-saving technology progress has had an overall promoting effect. However, the effect of energy-saving technology progress is not as effective as the scale efficiency through improving energy technology progress. (iii) Per capita value added and population size are powerful factors that promote carbon emissions. This study also makes a targeted analysis of the driving factors of carbon emissions in different industries, which provide a reference for the process of carbon-reduction in different industries. [Display omitted] • Investigating the effects of various technical factors on total carbon emission and carbon emission intensity. • Combining Index Decomposition Analysis and Production-theoretical Decomposition Analysis. • Ten driving factors of carbon emission in India's 56 sectors were decomposed. • Technique factors have not effectively offset Indian carbon emission. • The effect of technological progress on carbon efficiency needs to be improved. [ABSTRACT FROM AUTHOR]