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Greater knowledge of environmental impact has contributed to raising the awareness level on sustainability in supply chains. Organisations increasingly recognise the need to implement the sustainable packaging principles in warehouses to minimise their organisational environmental impact. Consequently, this study investigates the role of sustainable packaging in managing environmental impacts in supply chains, focusing on new-age packaging materials, such as recyclable and biodegradable packaging materials, and handling transportation with lower carbon footprints. The main goal of the research is to evaluate how sustainable packaging practices in warehouses influence waste minimisation, reduction of carbon emissions, and the overall performance of the supply chain, as well as to determine challenges and costs related to its implementation. This research employs both a survey method, whereby responses from 385 supply chain experts were analysed quantitatively, and qualitative analysis from literature reviews. It is intended to establish the current knowledge of sustainable packaging and further explore its adoption and application among businesses. The fits of models are verified with SmartPLS, and the results of the analysis with the IBM SPSS Statistics 27 show that the hypotheses have significant implications to reduce waste production and greenhouse emissions. The conclusion emphasises the necessity to develop and use sustainable packaging as a way of enhancing supply-chain environmental performance. These research findings are worth noting by policymakers, practitioners, and scholars in the field of environmental sustainability, by taking innovative packaging initiatives. It was found out that 89.4% of the respondents had already switched to sustainable packaging, and biodegradable materials were the most common (40.3%). Regression analysis showed a significant positive impact of sustainable packaging on waste reduction (Formula: see text, Formula: see text) and carbon emission reduction (Formula: see text, Formula: see text), affirming the model’s predictive value.
Gupta et al. (Mon,) studied this question.