Accurate power-sector carbon emission data (PS-CED) are critical for ensuring sustainable practices in carbon trading and effective emission reductions. However, conventional centralized reporting systems are susceptible to data tampering, duplicate accounting, and inefficient manual verification, hindering the achievement of sustainability goals. Blockchain technology (BCT) provides transparency, immutability, and automated compliance, offering significant potential for improving the sustainability of PS-CED supervision. Despite this, its diffusion in the sector faces challenges such as data heterogeneity, security concerns, institutional differences, and resource limitations. This study integrates the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to develop a diffusion framework for BCT adoption in PS-CED supervision with a focus on sustainability. Using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the study examines both linear effects and multiple adoption configurations. The results indicate that adoption willingness mediates the effects of perceived usefulness and ease of use, while perceived regulatory norms underscore policy pressure as a crucial external driver for fostering sustainability. Configurational analysis reveals heterogeneous diffusion patterns, with high adoption performance driven by technological capability combined with regulatory enforcement, and low performance linked to weak technological engagement and structural constraints. Based on these findings, a strategic framework is proposed to support differentiated and phased BCT adoption across organizational contexts to enhance sustainability in carbon emission supervision. This paper clarifies the diffusion mechanisms and provides practical guidance for scaling blockchain-based PS-CED supervision to promote sustainability.
Li et al. (Thu,) studied this question.