Chinelo Faustina Nzoiwu1, Charles Chikwendu Okpala2, Ugochukwu Richards Orji3
1 Lecturer, Mechanical Engineering Department, Federal Polytechnic, Oko, Nigeria.
2 Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.
3 Lecturer, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.
Abstract
Global supply networks are among the most powerful engines of economic growth, yet they remain a dominant source of greenhouse gas emissions through carbon-intensive freight transportation, upstream procurement activities, and linear material consumption systems. The attainment of net-zero supply chains therefore requires integrated, data-driven decision frameworks that are capable of simultaneously optimizing logistics operations, supplier portfolios, and circular resource recovery flows. This article provides a multidisciplinary optimization review of three interconnected decarbonization levers: net-zero freight systems, green procurement and sustainable supplier selection, as well as circular economy resource flow integration. Drawing on recent advances in mixed-integer linear programming, multi-objective optimization, digital twin modeling, and AI-enabled analytics, the study synthesizes methodological innovations that embed lifecycle carbon accounting directly into multi-echelon supply network design. Evidence from the literature indicates that isolated interventions such as freight modal shifts and routing optimization can reduce logistics emissions by 15–30%, while supplier decarbonization strategies yield embodied carbon reductions of 20–40%. Circular economy adoption through closed-loop logistics and secondary material substitution further enables lifecycle emission savings of 25–60% depending on recovery rates and sectoral context. Building on these insights, the article introduces the Carbon-Optimized Circular Freight-Procurement Network (CO-CFPN) framework, which demonstrates that fully integrated optimization across freight, sourcing, and circular flows can generate compounded emissions reductions of 35–60% relative to cost-only baseline networks. The paper concludes with a future research agenda that emphasizes interoperable carbon data infrastructures, hybrid AI–optimization systems, and policy-aligned modeling to accelerate scalable net-zero transitions across global supply networks.
Keywords: Net-zero freight; Low-carbon logistics; Green procurement; Supplier Optimization; Circular Economy, Carbon-aware supply chains; Multi-objective optimization
References
- Bektas, T. and Laporte, G. (2011) ‘The pollution-routing problem’, Transportation Research Part B: Methodological, 45(8), pp. 1232-1250.
- CDP (2022) Global supply chain report 2022. CDP Worldwide.
- Chukwumuanya, E.O., Okpala, C.C. and Udu, C.E. (2025) ‘Carbon accounting at the shop-floor: the integration of real-time energy monitoring, process modeling and LCA for net-zero targets’, Jurnal Teknik Indonesia, 4(1), pp. 28-41. https://jurnal.seaninstitute.or.id/index.php/juti/article/view/728
- Demir, E., Bektas, T. and Laporte, G. (2014) ‘A review of recent research on green road freight transportation’, European Journal of Operational Research, 237(3), pp. 775-793.
- Deswal, P. (2025) ‘Article 6 of the Paris Agreement: A comprehensive review of mechanisms, progress, and persistent challenges’, International Journal of Technology, Health and Sustainability, 1(2), pp. 111-125. https://ijths.com/wp-content/uploads/IJTHS-010235.pdf
- Deswal, S. and Deswal, P. (2025a) ‘Sustainability: Greenhouse Gas Protocol and global GHG emissions’ status and trends’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), pp. 2051-2063. https://doi.org/10.54660/.IJMRGE.2025.6.1.2051-2063
- Deswal, S. and Deswal, P. (2025b) ‘The Sustainable Development Goals (SDGs): A comprehensive review of progress, implementation, and the path forward’, International Journal of Technology, Health and Sustainability, 1(2), pp. 133-141. https://ijths.com/wp-content/uploads/IJTHS-010237.pdf
- Dubey, R., Gunasekaran, A., Childe, S.J., et al. (2021) ‘Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism’, Journal of Business Research, 124, pp. 315-328.
- Egwuagu, O.M., Okpala, C.C. and Udu, C.E. (2026) ‘Circular economy and net-zero manufacturing: a data-driven multidisciplinary framework for sustainable industrial transformation’, International Journal of Technology, Health and Sustainability, 2(2), pp. 540-550. https://ijths.com/wp-content/uploads/IJTHS-0202021.pdf
- EMF (2019) Completing the picture: how the circular economy tackles climate change. Ellen MacArthur Foundation.
- Genovese, A., Acquaye, A.A., Figueroa, A., et al. (2017) ‘Sustainable supply chain management and the transition towards a circular economy’, International Journal of Production Economics, 183, pp. 299-315.
- Govindan, K. and Soleimani, H. (2017) ‘A review of reverse logistics and closed-loop supply chains’, European Journal of Operational Research, 240(3), pp. 603-626.
- Govindan, K., Rajendran, S., Sarkis, J., et al. (2015) ‘Multi-criteria decision making approaches for green supplier evaluation and selection: a literature review’, Journal of Cleaner Production, 98, pp. 66-83.
- IEA (2023) Tracking transport 2023. Paris: International Energy Agency.
- IPCC (2023) Climate change 2023: mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
- IRP (2020) Resource efficiency and climate change: material efficiency strategies for a low-carbon future. International Resource Panel, United Nations Environment Programme, Nairobi.
- Ivanov, D. (2023) ‘The Industry 5.0 framework: viability-based integration of resilience, sustainability, and human-centricity perspectives’, International Journal of Production Research, 61(5), pp. 1683-1695.
- Ivanov, D. and Dolgui, A. (2020) ‘A digital supply chain twin for managing disruption risks and resilience’, International Journal of Production Research, 59(1), pp. 1-15.
- Kannan, D., Khodaverdi, R., Olfat, L., et al. (2013) ‘Integrated fuzzy multi-criteria decision-making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain’, Journal of Cleaner Production, 47, pp. 355-367.
- Kristoffersen, E., Blomsma, F., Mikalef, P., et al. (2020) ‘The smart circular economy: a digital-enabled circular strategies framework for manufacturing companies’, Journal of Business Research, 120, pp. 241-261.
- McKinnon, A. (2018) Decarbonizing logistics: distributing goods in a low carbon world. 1st ed. London: Kogan Page.
- Miettinen, K. (2012) Nonlinear multiobjective optimization. 1st ed. New York: Springer.
- Nwamekwe, C.O. and Okpala, C.C. (2025) ‘Circular economy strategies in industrial engineering: from theory to practice’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), pp. 1773-1782.https://www.allmultidisciplinaryjournal.com/uploads/archives/20250212103754_MGE-2025-1-288.1.pdf
- Nwankwo, C.O., Okpala, C.C. and Igbokwe, N.C. (2024) ‘Enhancing smart manufacturing supply chains through cybersecurity measures’, International Journal of Engineering Inventions, 13(12), pp. 1-6. https://www.ijeijournal.com/papers/Vol13-Issue12/13120106.pdf
- Nwosu, O.C., Okpala, C.C. and Igbokwe, N.C. (2026) ‘Digital twins for smart supply chain transformation: a multidisciplinary review’, International Journal of Technology, Health and Sustainability, 2(3), pp. 1005-1015. https://ijths.com/wp-content/uploads/IJTHS-0203009.pdf
- Ogboh, V.C., Obianyo, R.U. and Okpala, C.C. (2026) ‘A data-driven framework for smart manufacturing: IoT sensor electronics, digital twins, and predictive analytics for production optimization’, International Journal of Technology, Health and Sustainability, 2(3), pp. 931-941. https://ijths.com/wp-content/uploads/IJTHS-0202072.pdf
- Okpala, C.C. (2026) ‘Machine learning–enabled design of composite materials: scalable structure–processing–property relationships across applications’, International Journal of Technology, Health and Sustainability, 2(1), pp. 154-161. https://ijths.com/wp-content/uploads/IJTHS-020166.pdf
- Okpala, C.C. and Chukwumuanya, E.O. (2026) ‘Industry 5.0-enabled strategies for energy-efficient and carbon-neutral manufacturing systems’, International Journal of Technology, Health and Sustainability, 2(3), pp. 1092-1103. https://ijths.com/wp-content/uploads/IJTHS-0203021.pdf
- Sarkis, J., Zhu, Q. and Lai, K.H. (2011) ‘An organizational theoretic review of green supply chain management literature’, International Journal of Production Economics, 130(1), pp. 1-15.
- Seuring, S. and Müller, M. (2008) ‘From a literature review to a conceptual framework for sustainable supply chain management’, Journal of Cleaner Production, 16(15), pp. 1699-1710.
- Shaw, K., Shankar, R., Yadav, S.S., et al. (2012) ‘Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain’, Expert Systems with Applications, 39(9), pp. 8182-8192.
- Tachizawa, E.M. and Wong, C.Y. (2014) ‘Towards a theory of multi-tier sustainable supply chains: a systematic literature review’, Supply Chain Management: An International Journal, 19(5/6), pp. 643-663.
- Udu, C.E. and Okpala, C.C. (2025) ‘Circular economy in wastewater management: water reuse and resource recovery strategies’, International Journal of Latest Technology in Engineering, Management and Applied Science, 14(3), pp. 128-136. https://doi.org/10.51583/IJLTEMAS.2025.140300016
- Udu, C.E. and Okpala, C.C. (2026a) ‘Artificial intelligence–enabled resilient scheduling: a systematic review and research roadmap for digital twin and machine learning in disruption-aware operations’, International Journal of Technology, Health and Sustainability, 2(2), pp. 486-497. https://ijths.com/wp-content/uploads/IJTHS-0202014.pdf
- Udu, C.E. and Okpala, C.C. (2026b) ‘Engineering safety in complex systems: a data-driven and predictive framework for machine learning, human factors, and system dynamics integration’, International Journal of Technology, Health and Sustainability, 2(1), pp. 391-401. https://ijths.com/wp-content/uploads/IJTHS-020199-0.pdf
- Udu, C.E., Ejichukwu, E.O. and Okpala, C.C. (2025b) ‘The application of digital tools for supply chain optimization’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(3), pp. 308-316. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250508172828_MGE-2025-3-047.1.pdf
- Udu, C.E., Okpala, C.C. and Edeh, M.O. (2025a) ‘Global roadmap for circular economies: the integration of digital innovation, governance, and sustainable development goals’, International Journal of Industrial and Production Engineering, 3(4), pp. 1-17. https://journals.unizik.edu.ng/ijipe/article/view/6764
- Udu, C.E., Okpala, C.C. and Onukwuli, S.K. (2026) ‘Supply chain resilience in the age of global disruptions: AI-driven risk modeling and optimization frameworks for climate, conflict, and pandemic shocks’, International Journal of Engineering Research and Development, 22(3), pp. 109-120. https://www.ijerd.com/paper/vol22-issue3/2203109120.pdf
- Wamba, S.F., Gunasekaran, A., Akter, S., et al. (2020) ‘Big data analytics and firm performance: effects of dynamic capabilities’, Journal of Business Research, 70, pp. 356-365.
- Wang, Y., Zhang, D., Liu, Q., et al. (2022) ‘Towards enhancing the resilience of logistics networks to disruptions: a digital twin-based optimization approach’, Transportation Research Part E: Logistics and Transportation Review, 158, 102610.
- Zhang, Y., Xiong, Y. and Qi, J. (2019) ‘Carbon-constrained logistics network design with alternative fuel vehicles’, Transportation Research Part D: Transport and Environment, 67, pp. 113-127.
