Financing capacity planning with environmental considerations: A non-parametric analysis

•The trade-off between capacity expansion and financing is considered.•A new non-parametric framework is introduced with environmental considerations.•The under/over investment problems of traditional capacity planning are mitigated.•An empirical application is investigated to validate the proposed...

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Veröffentlicht in:Omega (Oxford) 2023-07, Vol.118, p.102866, Article 102866
Hauptverfasser: Yang, Jiawei, Li, Yuanyu, Fang, Lei
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Sprache:eng
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Zusammenfassung:•The trade-off between capacity expansion and financing is considered.•A new non-parametric framework is introduced with environmental considerations.•The under/over investment problems of traditional capacity planning are mitigated.•An empirical application is investigated to validate the proposed approach. Motivated by the challenge of efficient capacity planning among firms with limited resources, we explore the interplay between financing and production planning with environmental considerations. The separation of these decisions may lead to biased estimation of capacity utilization. Therefore, we develop a new two-stage data envelopment analysis approach for joint optimization of capacity investment and resource allocation, allowing for a finance-based expansion strategy given the pollution-generating technologies. Moreover, we provide a practical method to determine the lower and upper bounds to mitigate the under-investment or over-investment problems. Our findings reveal that the financing expansion practice can not only help to reduce inefficiencies resulted from conventional capacity planning strategies, but also provide an efficient technique to coordinate production and sustainability in a “win-win” situation. We illustrate the proposed approach by an empirical investigation to industrial production and wastewater treatment systems, the results validate the importance of adopting the financing expansion practice, especially for decision making units (regions) with high productivity.
ISSN:0305-0483
1873-5274
DOI:10.1016/j.omega.2023.102866