The influence of blockchain technology on circular economy implementation in the automotive sector: From a GMM model to a new machine learning algorithm

Woon Leong Lin*, Nelvin Xe Chung Leow, Wai Mun Lim, Ming Kang Ho, Jing Yi Yong, Chuen Khee Pek

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This investigation explores the integration of blockchain technology (BCT) with circular economy (CE) principles within the automotive sector, leveraging a dataset from the years 2011 to 2019. Employing advanced analytical techniques, including machine learning models and the system generalized method of moments (GMM), the study meticulously assesses BCT's impact on CE practices over the specified period. The dataset, curated from esteemed sources such as CSRHub, Thomson Reuters, and Bloomberg, enhances the reliability and validity of our analysis. Results indicate a positive influence of BCT on the adoption and effectiveness of CE practices in the automotive industry, suggesting that CE practices can bolster firm performance. Notably, the analysis reveals that support vector machines (SVM) and neural networks (NNs) exhibit superior efficacy over the random forest (RF) model in capturing the nuances of the BCT-CE interplay. This is evidenced by their lower root-mean-square error (RMSE) and mean absolute error (MAE), signifying greater predictive accuracy. The findings illuminate BCT's potential to revolutionize CE practices, optimize resource use, and foster sustainability in the automotive field.

Original languageEnglish
JournalBusiness Strategy and the Environment
DOIs
Publication statusPublished - 5 Nov 2024

ASJC Scopus subject areas

  • Business and International Management
  • Geography, Planning and Development
  • Strategy and Management
  • Management, Monitoring, Policy and Law

Keywords

  • automotive industry
  • blockchain technology
  • circular economy
  • machine learning
  • system GMM

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