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<p>Leather processing industries (LPIs) in India are constantly being closely examined for their environmental impact, and the Central Pollution Control Board (CPCB) has placed them in the red category. The concepts of the circular economy (CE), which promote closed-loop systems for material and non-material flows and convert waste into resources, provide a workable strategy for mitigating such impacts. However, different industries apply CE practices and enablers differently, and very little is known about how effective they are when it comes to LPIs. This study first performs a bibliometric analysis to find research trends in CE, followed by a content analysis to extract pertinent CE practices and enablers from the body of existing literature in different sectors. The analytic hierarchy process (AHP) was then used to prioritise the derived nine CE enablers for the specific nature of LPIs, and using data from 101 Indian LPIs, structural equation modelling (SEM) was used to evaluate the effects of the top four enablers on the environmental performance of LPIs. The SEM findings indicate that our top prioritised enabler policy/regulatory support and incentives (PO) have a significant impact on environmental performance of LPIs in India. The implications of this work may be divided into two categories: one for corporations and the other for policymakers. Policymakers in India must concentrate more on providing incentives with stringent legislation if they hope to see improved environmental performance from LPIs. Additionally, LPIs with limited funding might initially concentrate more on our analysed top-ranked enablers for improved environmental performance. This work may be limited by the fact that the indicators/observed variables we used for independent variables (enablers) and observed dependent variables (environmental performance) were not standardised according to the specific nature of LPIs, and hence future researchers may first carry out this work before analysing the environmental performance of LPIs.</p>

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An Integrated AHP-SEM Framework for Prioritising Circular Economy Enablers and Their Impact on Environmental Performance in Leather Processing Industries

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Pankaj Kumar Tyagi,
  • Sheeba Jilani,
  • Faisal Talib

摘要

Leather processing industries (LPIs) in India are constantly being closely examined for their environmental impact, and the Central Pollution Control Board (CPCB) has placed them in the red category. The concepts of the circular economy (CE), which promote closed-loop systems for material and non-material flows and convert waste into resources, provide a workable strategy for mitigating such impacts. However, different industries apply CE practices and enablers differently, and very little is known about how effective they are when it comes to LPIs. This study first performs a bibliometric analysis to find research trends in CE, followed by a content analysis to extract pertinent CE practices and enablers from the body of existing literature in different sectors. The analytic hierarchy process (AHP) was then used to prioritise the derived nine CE enablers for the specific nature of LPIs, and using data from 101 Indian LPIs, structural equation modelling (SEM) was used to evaluate the effects of the top four enablers on the environmental performance of LPIs. The SEM findings indicate that our top prioritised enabler policy/regulatory support and incentives (PO) have a significant impact on environmental performance of LPIs in India. The implications of this work may be divided into two categories: one for corporations and the other for policymakers. Policymakers in India must concentrate more on providing incentives with stringent legislation if they hope to see improved environmental performance from LPIs. Additionally, LPIs with limited funding might initially concentrate more on our analysed top-ranked enablers for improved environmental performance. This work may be limited by the fact that the indicators/observed variables we used for independent variables (enablers) and observed dependent variables (environmental performance) were not standardised according to the specific nature of LPIs, and hence future researchers may first carry out this work before analysing the environmental performance of LPIs.