Abstract
This paper presents a bibliometric analysis and literature review of green warehousing (GW) within the context of sustainability and the energy transition. This study analyses 95 peer-reviewed publications from Scopus (2007–2024) to examine research trends, key themes, and methodological approaches. Using VOSviewer, the study identifies major clusters around energy efficiency, consumption, optimisation, and renewable energy use. Interest in GW has increased significantly since 2015, particularly in engineering and computer science, with substantial contributions from Italy, China, and India. While quantitative optimisation dominates, integrating decision-support tools, life cycle assessments, and interdisciplinary methods remains limited. Social science engagement is also lacking. The review calls for more empirical, practice-based research on warehouse technologies and intelligent energy systems within circular and green supply chain models. This study addresses a key gap by linking GW to energy-focused sustainability and offers a replicable framework to guide future research in the global green transition.
References
Akandere, G. (2017). The effect of logistics businesses' green warehouse management practices on business performance. Proceedings of the 25th International Academic Conference. OECD Headquarters, Paris, 10-23. https://doi.org/10.20472/iac.2016.025.002
Aveyard, H. (2014). Doing a literature review in health and social care : a practical guide. New York: McGraw-Hill.
Bartolini, M., Bottani, E., & Grosse, E. H. (2019). Green warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production, 226, 242–258. https://doi.org/10.1016/j.jclepro.2019.04.055
Bhandigani, M., Pattan, A., & Carpitella, S. (2024). Strategic roadmap for adopting data-driven proactive measures in solar logistics. Applied Sciences, 14(10), 4246. https://doi.org/10.3390/app14104246
Cannava, L., Javan, F. D., Najafi, B., & Perotti, S. (2024). Green warehousing practices: Assessing the impact of PV self-consumption enhancement strategies in a logistics warehouse. Sustainable Energy Technologies and Assessments, 72, 104054. https://doi.org/10.1016/j.seta.2024.104054
Chen, H., Zhao, D., Li, J., Zhang, L., Shen, T., & Yin, Y. (2024). A study on the exploration of the development process of regenerative applications of energy technologies in industrial warehouse buildings: Bibliometric research from 2004 to 2024. Buildings, 14(12), 4019. https://doi.org/10.3390/buildings14124019
Chiang, K. L. (2024). Optimising warehouse building design for simultaneous revenue generation and carbon reduction in Taiwan: A Fuzzy Nonlinear Multi-Objective Approach. Buildings, 14(8), 2441. https://doi.org/10.3390/buildings14082441
Cosma, A., Conte, R., Solina, V., & Ambrogio, G. (2024). Design of KPIs for evaluating the environmental impact of warehouse operations: a case study. Procedia Computer Science, 232, 2701–2708. https://doi.org/10.1016/j.procs.2024.02.087
Daniel, J., & Dissanayake, C. K. (2021). Decarbonising supply chain operations. In M. Fargnoli, M. Lombardi, M. Tronci, P. Dallasega, M. M. Savino, F. Costantino, G. Di Gravio, & R. Patriarca (Eds.), Proceedings - 4th European Rome Conference 2021 (pp. 1421-1422). https://doi.org/10.1016/j.ejor.2006.07.009
Dimitrov, L., & Saraceni, A. (2023). Ranking model to measure energy efficiency for warehouse operations sustainability. Journal of Cleaner Production, 428, 139375. https://doi.org/10.1016/j.jclepro.2023.139375
Đukić, G., Česnik, V., & Opetuk, T. (2010). Order-picking methods and technologies for greener warehousing. Strojarstvo, 52(1), 23-31. https://www.researchgate.net/publication/286952752_Order-picking_Methods_and_Technologies_for_Greener_Warehousing
Ene, S., Küçükoğlu, İ., Aksoy, A., & Öztürk, N. (2016). A genetic algorithm for minimising energy consumption in warehouses. Energy, 114, 973-980. https://doi.org/10.1016/j.energy.2016.08.045
Fahimnia, B., Sarkis, J., & Eshragh, A. (2015). A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis. Omega, 54, 173–190. https://doi.org/10.1016/j.omega.2015.01.014
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21. https://doi.org/10.1016/j.ejor.2006.02.025
Hämäläinen, R. P., Lindstedt, M. R., & Sinkko, K. (2000). Multiattribute risk analysis in nuclear emergency management. Risk Analysis, 20(4), 455–468. https://doi.org/10.1111/0272-4332.204044
Jensen, S. S. (2016). Energy performances of low charge NH3 systems in practice. Refrigeration Science and Technology. https://doi.org/10.18462/iir.iccc.2016.0064
Kozar, Ł., & Wodnicka, M. (2024). Blockchain in energy: Literature review in the context of sustainability. Economics and Environment, 90(3), 866. https://doi.org/10.34659/eis.2024.90.3.866
Lei, M. (2024). Application of energy sustainability model based on optical sensing technology in intelligent warehousing performance management in the green manufacturing industry. Thermal Science and Engineering Progress, 54, 102789. https://doi.org/10.1016/j.tsep.2024.102789
Lewczuk, K., Kłodawski, M., & Gepner, P. (2021). Energy consumption in a distributional warehouse: A practical case study for different warehouse technologies. Energies, 14(9), 2709. https://doi.org/10.3390/en14092709
Li, Z., Zheng, D., Li, X., Liu, X., & Wu, Y. (2024). Research on Adaptive System of Warehouse Energy Management System Using Gradient Boosting Tree Algorithm. 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems, ICPICS 2024, 1643–1648. https://doi.org/10.1109/icpics62053.2024.10796830
López-Montero, D., Hernando-Sánchez, P., Limones-Andrade, M., García-Navarro, A., Valverde, A., Parra, J. M. S., & Auñón, J. M. (2024). Differentiable programming for gradient-based control and optimisation in physical systems. Sustainable Energy, Grids and Networks, 39, 101495. https://doi.org/10.1016/j.segan.2024.101495
Luu, M. (2016). Developing the implementation of green warehousing at IKEA Finland. [Bachelor's Thesis]. Degree Programme in International Business. Haaga-Helia University of Applied Sciences. https://core.ac.uk/download/pdf/38137917.pdf
Lv, J., Li, Y., Huang, G., & Li, Y. (2022). Planning the economy-energy-environment nexus system: A case study of Pearl River Delta, China. ACM International Conference Proceeding Series, 75–81. https://doi.org/10.1145/3533254.3533269
Marchet, G., Melacini, M., & Perotti, S. (2015). Investigating order picking system adoption: a case-study-based approach. International Journal of Logistics Research and Applications, 18(1), 82–98. https://doi.org/10.1080/13675567.2014.945400
Marchi, B., Zanoni, S., & Jaber, M. Y. (2020). Energy implications of lot sizing decisions in refrigerated warehouses. Energies, 13(7), 1739. https://doi.org/10.3390/en13071739
Mashud, A. H. M., Roy, D., Chakrabortty, R. K., Tseng, M. L., & Pervin, M. (2022). An optimum balance among the reduction in ordering cost, product deterioration and carbon emissions: A sustainable green warehouse. Environmental Science and Pollution Research, 29, 78029–78051. https://doi.org/10.1007/s11356-022-21008-0
McLay, A., Morant, G., Breisch, K., Rodwell, J., & Rayburg, S. (2024). Practices to improve the sustainability of Australian cold storage facilities. Sustainability, 16(11), 4584. https://doi.org/10.3390/su16114584
Md Mashud, A. H., Pervin, M., Mishra, U., Daryanto, Y., Tseng, M. L., & Lim, M. K. (2021). A sustainable inventory model with controllable carbon emissions in green-warehouse farms. Journal of Cleaner Production, 298, 126777. https://doi.org/10.1016/j.jclepro.2021.126777
Meneghetti, A., & Monti, L. (2013). Sustainable storage assignment and dwell-point policies for automated storage and retrieval systems. Production Planning & Control, 24(6), 511-520. https://doi.org/10.1080/09537287.2011.637525
Meneghetti, A., & Monti, L. (2015). Greening the food supply chain: An optimisation model for sustainable design of refrigerated automated warehouses. International Journal of Production Research, 53(21), 6567–6587. https://doi.org/10.1080/00207543.2014.985449
Modica, T., Perotti, S., & Melacini, M. (2021). Green warehousing: Exploration of organisational variables fostering the adoption of energy-efficient material handling equipment. Sustainability, 13(23), 13237. https://doi.org/10.3390/su132313237
Nightingale, J. M., & Marshall, G. (2012). Citation analysis as a measure of article quality, journal influence and individual researcher performance. Radiography, 18(2), 60–67. https://doi.org/10.1016/j.radi.2011.10.044
Oloruntobi, O., Mokhtar, K., Mohd Rozar, N., Gohari, A., Asif, S., & Chuah, L. F. (2023). Effective technologies and practices for reducing pollution in warehouses - A review. Cleaner Engineering and Technology, 13, 100622. https://doi.org/10.1016/j.clet.2023.100622
Pham, A., Jin, T., Novoa, C., & Qin, J. (2019). A multi-site production and microgrid planning model for net-zero energy operations. International Journal of Production Economics, 218, 260–274. https://doi.org/10.1016/j.ijpe.2019.04.036
Ries, J. M., Grosse, E. H., & Fichtinger, J. (2017). Environmental impact of warehousing: a scenario analysis for the United States. International Journal of Production Research, 55(21), 6485–6499. https://doi.org/10.1080/00207543.2016.1211342
Sandra, M., Narayanamoorthy, S., Ferrara, M., Innab, N., Ahmadian, A., & Kang, D. (2024). A novel decision support system for the appraisal and selection of green warehouses. Socio-Economic Planning Sciences, 91, 101782. https://doi.org/10.1016/j.seps.2023.101782
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Tappia, E., Marchet, G., Melacini, M., & Perotti, S. (2015). Incorporating the environmental dimension in the assessment of automated warehouses. Production Planning and Control, 26(10), 824–838. https://doi.org/10.1080/09537287.2014.990945
Tiwari, S., Daryanto, Y., & Wee, H. M. (2018). Sustainable inventory management with deteriorating and imperfect quality items considering carbon emission. Journal of Cleaner Production, 192, 281–292. https://doi.org/10.1016/j.jclepro.2018.04.261

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