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Emergy based sustainability evaluation model for retired machineries integrating energy, environmental and social factors

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  • Zhang, Xugang
  • Xu, Lu
  • Zhang, Hua
  • Jiang, Zhigang
  • Wang, Yan

Abstract

The rapid development of technology and innovation has made the performance of the new-generation machinery generally better than that of retired machinery. Simply using remanufacturing technology to bring retired machinery back into specifications of previous life will lead to reduced demands from consumers because of the out-of-date functioning remanufactured products. This technology gap (TG) is rarely considered in existing researches. This paper proposes an emergy based sustainability assessment model for retired machinery to solve this problem. Firstly, the TG between retired and new-generation machinery is quantified as functional devaluation from the perspectives of energy, environment and society through emergy theory. Secondly, the added value of retired machinery is derived from the evaluation of remanufacturing cost. Among them, the remanufacturing cost is predicted based on the BP Neural Networks. Then, this paper combines the functional devaluation and added value of retired mechanery to calculate the sustainability indicator. Finally, the feasibility of this study was verified by the sustainability evaluation of the WD615.50 diesel engine, and the results show that the WD615.50 diesel engine has no potential for re-service.

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  • Zhang, Xugang & Xu, Lu & Zhang, Hua & Jiang, Zhigang & Wang, Yan, 2021. "Emergy based sustainability evaluation model for retired machineries integrating energy, environmental and social factors," Energy, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:energy:v:235:y:2021:i:c:s0360544221015796
    DOI: 10.1016/j.energy.2021.121331
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    References listed on IDEAS

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    1. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    2. Song, Yazhi & Li, Yin & Liu, Tiansen, 2023. "Carbon asset remolding and potential benefit measurement of machinery products in the light of lean production and low-carbon investment," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

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