Paving the way to environmental sustainability: A systematic review to integrate big data analytics into high-stake decision forecasting

Rohit Agrawal, Nazrul Islam, Ashutosh Samadhiya, Vinaya Shukla, Anil Kumar and Arvind Upadhyay

Technological Forecasting and Social Change, 2025, vol. 214, issue C

Abstract: Big Data Analytics (BDA) is increasingly gaining interest in supply chain management due to the incorporation of digital technology in a range of operations. It facilitates the movement of commodities and data efficiently. However, despite the numerous benefits associated with BDA, there has been limited research on the extent to which BDA can improve environmental sustainability in supply chains. In an attempt to assess the depth of our knowledge, this study undertakes a bibliometric analysis in which 155 relevant articles are retrieved. The assessment discloses the various factors driving, limiting, and stimulating the adoption of BDA in the digital supply chain through analysis and discussion. Additionally, it suggests a framework linking the factors to achieve environmental sustainability. The outcomes of the evaluation indicate that the adoption of BDA could help in realizing an eco-friendly supply chain by reducing the carbon footprint, increasing product life cycles, minimizing the cost of transportation, and reducing transport-related emissions. This research suggests that policymakers should support BDA technology adoption for the reasons identified - it assists in boosting innovation and resilience in the increasingly competitive, ever changing market and the chaotic economic conditions of some industries. Many decisions made regarding environmental sustainability call for policies that will encourage BDA use to address climate, resources, energy management and sustainability factors.

Keywords: Supply chain management; Decision forecast; Big data analytics; Drivers; Barriers; Sustainability (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:214:y:2025:i:c:s0040162525000915

DOI: 10.1016/j.techfore.2025.124060

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