Impact of bad outputs and environmental regulation on efficiency of Indian leather firms: a directional distance function approach

Singh, Aparajita; Gundimeda, Haripriya

doi:10.1080/09640568.2020.1822307

Cited by 14 publications

(6 citation statements)

References 43 publications

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“…Our results support the previous literature on efficiency measurement that there is significant scope of reducing inputs like energy and material consumption in the leather industry (Bhandari, 2012; Bhandari and Maiti, 2012; Gupta et al , 2019). Studies have attributed inefficient use of resources in leather industry (like increased material consumption) to lack of research and development of indigenous technology, use of conventional technologies and inadequate emphasis on human skill development (Schjolden, 2000; Department of Industrial Policy & Promotion, 2012; Singh and Gundimeda, 2021). Central Pollution Control Board (2019) elaborates on techniques to reduce water and energy use, recovery of chemicals and residue management, adoption of cleaner technologies and other sustainable practices to help firms achieve the potential input savings.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

Analysing drivers of efficiency in the leather industry: a two-stage double bootstrap DEA approach

Singh

Gundimeda

2021

BIJ

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PurposeThe Indian leather industry contributes to economic growth at a significant environmental cost. Due to the rising global demand for sustainable leather products, promoting efficient input utilisation has become vital. This study measures input efficiency and its determinants for leather industry in order for it to improve its future performance.Design/methodology/approachIn the first stage, bootstrap data envelopment analysis (DEA) approach is used for measuring efficiency and analysing firms' differences based on their geographical location, organisational structures, urban-rural location and sub-industrial groups. A second stage regression examines efficiency determinants using size, age, skill and capital-labour intensity as the explanatory variables.FindingsEfficiency result shows a significant potential of minimising inputs by 47% provided the firms adopt best practices. West Bengal firms, urban located firms, individual and proprietorship owned firms and leather consumer goods firms are found to be relatively efficient to their counterparts. Size, skilled managerial staff and labour-intensive firms positively affect efficiency.Practical implicationsConstruction of well-connected roads for accessing urban retail markets and provision of reliable electricity would improve efficiency of rural firms. Small-scale enterprises have a larger share in Indian leather industry; therefore, policy should focus on enhancing the firms' scale and investing in training facilities to skill employed labour for ensuring optimal use of inputs.Originality/valuePrevious studies on the leather industry have used the conventional DEA efficiency measurement approach. This study uses DEA bootstrapping model for robust efficiency estimates and provides consistent inferences about the determinants.

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Section: Empirical Results and Discussionmentioning

confidence: 99%

Analysing drivers of efficiency in the leather industry: a two-stage double bootstrap DEA approach

Singh

Gundimeda

2021

BIJ

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“…Li et al (2020) used a meta-frontier nonradial DDF approach to measure the static and dynamic CO2 emission performance of 16 port enterprises in China covering the years of 2013 to 2018. Singh and Gundimeda (2021) measured the environmental efficiency of the grossly polluting Indian leather industry using DDF method under three directional vectors.…”

Section: Environmental Efficiency At the Industrial/sectoral Levelmentioning

confidence: 99%

The average environmental efficiency technique and its application to Chinese provincial panel data

Tang

Yang

Wei

2022

Environ Sci Pollut Res

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“…In this study, we use the super-efficiency DEA model to estimate WRUE. Referring to relevant literature on ER and WRUE (Dadabaev, 2016;Han et al, 2020;Singh and Gundimeda, 2021) and considering the availability of data, we select capital (X 1 ), labor (X 2 ), water (X 3 ), GDP (Y 1 ) and WD (Y 2 )capital, labor and water are input variables, and GDP and WD are output variables. The input and output variables are shown in the table below (Table 1).…”

Section: Super-efficiency Dea Modelmentioning

confidence: 99%

The impact of environmental regulation on water resources utilization efficiency

Wang

2022

Front. Environ. Sci.

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With the rapid development of economy and the increase of population, water consumption has increased in China. Meanwhile, water waste, water pollution and uneven distributions of water resources have posed a great challenge to China’s economic development. Hence, the improvement of water resources utilization efficiency has become an important measure to solve the shortage of water resources. In this study, the super efficiency Data envelopment analysis model and Tobit model are adopted to study the impact of environmental regulation on water resources utilization efficiency. The results show that water resources efficiency score is different in different regions in China, where the efficiency score in the eastern region is the highest, followed by the western region and the central region. Environmental regulation negatively affects water resources utilization efficiency and these effects are different effects in different regions. Environmental regulation affects three variables, foreign direct investment, technological innovation, industrial structure, and then affects water resources utilization efficiency through these variables. Based on the above research, we put forward some policy recommendations for increasing environmental regulation intensity to improve water resources utilization efficiency.

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Impact of bad outputs and environmental regulation on efficiency of Indian leather firms: a directional distance function approach

Cited by 14 publications

References 43 publications

Analysing drivers of efficiency in the leather industry: a two-stage double bootstrap DEA approach

Analysing drivers of efficiency in the leather industry: a two-stage double bootstrap DEA approach

The average environmental efficiency technique and its application to Chinese provincial panel data

The impact of environmental regulation on water resources utilization efficiency

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