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Modelling and analysis of energy efficiency drivers by fuzzy ISM and fuzzy MICMAC approach

Author

Listed:
  • Rahul Sindhwani
  • Varinder Kumar Mittal
  • Punj Lata Singh
  • Vivek Kalsariya
  • Faizan Salroo

Abstract

The need nowadays has arisen which has forced today's generation to sit and think about taking pains regarding energy efficiency. This huge wastage of energy and the ever-increasing cost have not only hindered the smooth economical run but also have sent huge Greenhouse gas emissions into the environment. So, it has become inescapable for us to turn a blind eye. Keeping this thing in mind, this research paper presents identification drivers of energy efficiency which helps us in making a framework which could guide industries in applying the energy efficiency in their setups. These 17 drivers were levelled and their effect was noticed through fuzzy interpretive structural modelling (ISM) and fuzzy MICMAC analysis. The results reveal that inadequate availability of energy, excessive losses, and energy audits have high driving power and low dependence power and are at the lowest level in fuzzy ISM digraph, while government promotional benefits, management commitment, research development, and planning for future drivers have high dependence power and low driving power. The results were clear enough to suggest that which drivers we have to take in attention in order to ensure that the energy efficiency is applied in an industry.

Suggested Citation

  • Rahul Sindhwani & Varinder Kumar Mittal & Punj Lata Singh & Vivek Kalsariya & Faizan Salroo, 2018. "Modelling and analysis of energy efficiency drivers by fuzzy ISM and fuzzy MICMAC approach," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 25(2), pages 225-244.
    • Handle: RePEc:ids:ijpqma:v:25:y:2018:i:2:p:225-244
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    Citations

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    Cited by:

    1. Sorooshian, Shahryar & Tavana, Madjid & Ribeiro-Navarrete, Samuel, 2023. "From classical interpretive structural modeling to total interpretive structural modeling and beyond: A half-century of business research," Journal of Business Research, Elsevier, vol. 157(C).
    2. Nalluri, Venkateswarlu & Chen, Long-Sheng, 2024. "Modelling the FinTech adoption barriers in the context of emerging economies—An integrated Fuzzy hybrid approach," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    3. Kelly M. Smith & Stephen Wilson & Paul Lant & Maureen E. Hassall, 2022. "How Do We Learn about Drivers for Industrial Energy Efficiency—Current State of Knowledge," Energies, MDPI, vol. 15(7), pages 1-26, April.
    4. Agarwal, Richa & Bhadauria, Artee & Kaushik, Hans & Swami, Sanjeev & Rajwanshi, Rohit, 2023. "ISM-MICMAC-based study on key enablers in the adoption of solar renewable energy products in India," Technology in Society, Elsevier, vol. 75(C).
    5. Naim Ahmad, 2024. "SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC," Sustainability, MDPI, vol. 16(20), pages 1-22, October.

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