School of the Environment

Hadi Shabanpour

PhD candidate
Room 318a, Steele Building (#03)
Hadi Shabanpour

Researcher biography

Hadi Shabanpour is a Ph.D. student in Environmental Management at the School of Environment (SENV) at the University of Queensland (UQ). Prior to joining UQ, he was a researcher for the "Young Researchers and Elites Club" at the IAU Karaj branch (KIAU) for eight years. He also lectured at IAU and the University of Applied Sci. & Tech. in Iran. He obtained a BSc degree in Industrial Management from KIAU and his master’s in strategic management from the IAU-Central Tehran Branch.

Research Interests

His research is interdisciplinary and focuses on the assessment of DMUs' sustainability and circularity as well as industrial eco-efficiency through DEA models. Specifically, he investigates real-world and state-of-the-art frameworks for assessing and predicting DMUs' sustainability and eco-efficiency, as well as benchmarks for practical improvement.

Journal Articles

  1. Shabanpour, H., Dargusch, P., Wadley, D., Saen, R. F. & Lieske, S.N. (2024). A breakthrough in macro-scale circularity and eco-efficiency assessment: A case study of OECD countries, Journal of Environmental Management, 360, 121070. (Q1), https://doi.org/10.1016/j.jenvman.2024.121070.
  2. Shabanpour, H., Dargusch, P., Wadley, D., & Saen, R. F. (2024). A breakthrough in the circular economy: Using a closed-loop framework to assess the circularity of supply chains, Journal of Cleaner Production, 444, 141265. (Q1), https://doi.org/10.1016/j.jclepro.2024.141265.
  3. Yousefi, S., Shabanpour, H., Ghods, K., Farzipoor Saen, R. (2023), “How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis", Computers & Industrial Engineering, 176, 108933. (Q1), https://doi.org/10.1016/j.cie.2022.108933. 
  4. Azadi, M., Yousefi, S., Farzipoor Saen, R., Shabanpour, H., Jabeen, F. (2023), “Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis", Journal of Business Research, 154, 113357. (Q1), https://doi.org/10.1016/j.jbusres.2022.113357. 
  5. Shabanpour, Hadi., Yousefi, Saeed., Farzipoor Saen, Reza., (2021), “Forecasting sustainability of supply chains in the circular economy context: A dynamic network data envelopment analysis and artificial neural network approach", Journal of Enterprise Information Management, Vol. 17, No. 1-3, (Q1), https://doi.org/10.1108/JEIM-12-2020-0494. 
  6. Yousefi, Saeed., Shabanpour, Hadi., Farzipoor Saen, Reza., (2021), "Sustainable clustering of customers using capacitive artificial neural networks: A case study in Pegah Distribution Company", RAIRO Operations Research, 55(1), 51-60. (Q2), https://doi.org/10.1051/ro/2020059. 
  7. Shabanpour, Hadi., Fathi, Amirali., Yousefi, Saeed., Farzipoor Saen, Reza., (2019) "Ranking sustainable suppliers using congestion approach of economies of scale theory of data envelopment analysis", Journal of Cleaner Production, Vol. 240, pp. 118190. (Q1), https://doi.org/10.1016/j.jclepro.2019.118190. 
  8. Shabanpour, Hadi., Yousefi, Saeed., Farzipoor Saen, Reza., (2017), “Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks”, Journal of Cleaner Production, Vol. 142, No. 2, pp. 1098–1107. (Q1), https://doi.org/10.1016/j.jclepro.2016.08.147. 
  9. Shabanpour, Hadi., Yousefi, Saeed., Farzipoor Saen, Reza., (2017), “Future planning for benchmarking and ranking sustainable suppliers using goal programming and robust double frontiers DEA”, Transportation Research Part D: Transport and Environment, Vol. 50, pp. 129-143. (Q1), https://doi.org/10.1016/j.trd.2016.10.022. 
  10. Shokri Kahi, V., Yousefi, S., Shabanpour, H., Farzipoor Saen, R., (2017), "How to evaluate sustainability of supply chains? A dynamic network DEA approach", Industrial Management & Data Systems, Vol. 117, No. 9, pp.1866-1889. (Q1), https://doi.org/10.1108/IMDS-09-2016-0389. 
  11. Tavana, Madjid., Shabanpour, Hadi., Yousefi, Saeed., Farzipoor Saen, Reza., (2017), “A Hybrid Goal Programming and Dynamic Data Envelopment Analysis Framework for Sustainable Supplier Evaluation”, Neural Computing and Applications, Vol. 28, No. 12, pp. 3683–3696. (Q1), https://doi.org/10.1007/s00521-016-2274-z. 
  12. Yousefi, Saeed., Shabanpour, Hadi., Fisher, Ron., Farzipoor Saen, Reza., (2016), “Evaluating and ranking sustainable suppliers by robust dynamic data envelopment analysis”, Measurement, Vol. 83, pp. 72-85. (Q1), https://doi.org/10.1016/j.measurement.2016.01.032. 
  13. Yousefi, Saeed., Shabanpour, Hadi., Farzipoor Saen, Reza., (2015), “Selecting the best supply chain by goal programming and network data envelopment analysis”, RAIRO Operations Research, Vol. 45, No. 3, pp. 601-617. (Q2), https://doi.org/10.1051/ro/2014059. 
  14. Yousefi, Saeed., Shabanpour, Hadi., Farzipoor Saen, Reza., Faramarzi, Gholam Reza., (2014), “Making an ideal decision-making unit using virtual network data envelopment analysis approach”, Int. J. Business Performance Management, Vol. 15, No. 4, pp. 316-328. (Q3).

More information

Project

Title: Promoting Macro-scale Circular Economy Assessments in Alignment with Sustainable Development Goals: A Case Study on OECD Countries

Description:
Macro-scale circularity assessment serves as a key tool within the Circular Economy (CE) paradigm, facilitating the monitoring and adjustment of nations' environmental endeavors towards the Sustainable Development Goals (SDGs) 2030 and fulfilling commitments outlined in the Paris Climate Agreement. However, despite its utility, assessing macro-level circularity presents inherent complexities due to the multifaceted nature of the issue (Valls-Val et al., 2022). As highlighted by dos Santos Gonçalves and Campos (2022), there exists a dearth of real-world frameworks for national-scale circularity performance assessments. Establishing practical benchmarks is imperative to promote circularity efforts, particularly in less sustainable nations. Consequently, extant literature advocates a transition from conceptual macro-scale circularity/CE studies and benchmarks toward actionable outcomes. A comprehensive evaluation of national circularity performance necessitates the consideration of diverse metrics encompassing key circularity domains (Smol et al., 2017). Methodologically, transitioning from theoretical to practical assessments, particularly when dealing with macro-level Decision-Making Units (DMUs), poses challenges, notably in integrating dual-role and bidirectional carryovers into the evaluation framework. This task is inherently complex due to the intricate interplay among various inter-stage/inter-period inputs and outputs across sustainability dimensions (Azadi et al., 2023). There is a call for unified evaluation methodologies to address the paucity of practical frameworks and gauge the alignment of nations' circularity endeavors with the SDGs. Accordingly, future research endeavors should concentrate on formulating macro-level benchmarks capable of monitoring national-level CE progression in alignment with the SDGs.

From a managerial perspective, the present project conducts a comparative analysis between circularity performance and eco-efficiency outcomes of nations, thereby shedding light on eco-friendly and sustainable trends within the OECD. Such an analysis not only offers insights into nations' progress towards the SDGs but also highlights the role of scholars in advancing the 2030 Agenda for Sustainable Development and the Paris Climate Agreement based on SDG #17. Within this context, the current project is inspired by the 'Planet Section' of the agenda, which encourages researchers to contribute to global sustainability efforts. In this regard, SDG #17 emphasizes the significance of international academic collaboration in realizing the SDGs, emphasizing knowledge exchange as a cornerstone (Elalfy et al., 2021). Furthermore, the project aligns with several SDGs, notably SDG #7, the transition to sustainable energy sources, SDG #11, the creation of sustainable urban environments, and SDG #12, responsible consumption and production patterns. 

Funding: Research Training Program (RTP)
Advisory Team: Dr Scott Lieske, Associate Professor Paul Dargusch, Dr David Wadley and Prof. Reza Farzipoor Saen
Duration: April 2022–September 2025