Convergence of Blockchain, Artificial Intelligence, and Internet of Things (BlockIoTIntelligence) with Metaverse for Industry 4.0 to Realize Net Zero Economy

Pham Quang Huy () and Vu Kien Phuc ()
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Pham Quang Huy: University of Economics Ho Chi Minh City (UEH)
Vu Kien Phuc: University of Economics Ho Chi Minh City (UEH)

A chapter in Net Zero Economy, Corporate Social Responsibility and Sustainable Value Creation, 2024, pp 187-202 from Springer

Abstract: Abstract Digital technologies will play a significant role in net zero transition. The Metaverse may result in significant reductions in carbon emissions. Blockchain-enabled Intelligent Internet of Things architecture with Artificial Intelligence (BlockIoTIntelligence) is verified to address current issues like centralized architecture, security, and privacy concerns, resource limitations, and a lack of training data. In light of these findings, the current study aims to investigate the effects of convergence between BlockIoTIntelligence and Metaverse (CBM) on sustainable net zero economy implementation (SNZEI). Statistical information from a survey given to a cross-sectional sample of 783 respondents in small and medium-sized enterprises (SMEs) is used to support the proposed model, which is investigated using structural equation modeling. The outcome of this study confirms the significantly positive interconnection between CBM and SNZEI. The current research makes contributions to the area by offering new perspectives for pursuing this line of inquiry together with strong empirical support, as the study on the impact of advanced technologies on SNZEI has only recently begun. Additionally, this research is a ground-breaking endeavor to provide managerial and operational pointers and offers a roadmap, at the policy degree, regarding the application of digital technologies to practitioners and policymakers, at the industrial and national levels.

Keywords: Artificial intelligence; Blockchain; Internet of Things; Metaverse; Net zero economy (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-55779-8_12

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