Towards spatially explicit carbon accounting for electricity: an in-depth study on integrated three-layer framework for hierarchical-zonal grid decarbonization

Abstract

The accurate quantification of grid carbon emission factors (CEFs) is foundational for robust carbon accounting, effective climate policy, and credible corporate sustainability reporting. Traditional national-average CEFs are fundamentally inadequate, perpetuating the “copper plate” fallacy by ignoring profound spatial and temporal heterogeneity within interconnected power systems. This review critically evaluates the emerging paradigm of “grid hierarchical and zonal division” as a necessary response to this challenge. We systematically analyze the limitations of existing methodologies, highlighting the significant gap between top-down administrative calculations and bottom-up physical flow tracing. As our central contribution, we propose an integrated three-layer framework that synthesizes established but previously siloed concepts, physical flow modeling, policy boundary definition, and data architecture, into a unified structure. The novelty lies in their explicit integration and the modeling of interdependencies across layers: a Physical Flow Layer (‘engine’), a Policy Boundary Layer (‘rulebook’), and a Data and Calculation Layer (‘nervous system’). Our comparative analysis demonstrates that no single methodology is universally superior; a strategic, hybrid application across the hierarchy is essential. A structured case-based analysis applying the framework to China’s West-East Electricity Transfer corridor demonstrates its practical utility, with illustrative estimates from published comparative analyses suggesting that different accounting choices for cross-border electricity can result in differences of 15%–30% in an importing region’s reported Scope 2 emissions. The review identifies critical challenges data transparency, treatment of electricity imports, and lack of standardization, and proposes actionable pathways. Future research frontiers include dynamic real-time CEFs, artificial intelligence for forecasting and zoning, and blockchain for data integrity. This framework provides an essential blueprint for next-generation grid CEFs indispensable for guiding a precise and efficient energy transition.