Key Takeaways & Executive Findings
- •• • The proposed GC-carbon reduction association model systematically classifies GC transaction types and establishes corresponding accounting criteria, resolving the double-counting deficiency where China's current regional grid emission factors fail to deduct renewable energy environmental attributes already transferred through GC transactions—a flaw that inflates renewable energy carbon reduction claims by up to 100% for the same green power. • • Kalman filtering and inversion techniques are applied to thermal power plant carbon emission monitoring data, improving data accuracy for source-side carbon accounting. This addresses the critical industrial bottleneck where direct measurement data from fossil fuel combustion contains noise and systematic errors that propagate through the entire accounting chain, affecting compliance and trading decisions. • • The differentiated accounting framework based on transaction characteristics enables spatial matching between GC transactions and regional power grids, operationalized through the IEEE 30-bus system and a geographic region in China. This provides a replicable methodology for mapping market-based environmental attribute transfers onto physical grid infrastructure, essential for implementing Japan's dual emission factor system (basic and adjusted factors) in China's context. • • The framework establishes a mapping relationship between the power network and geographic regions, enabling carbon accounting that distinguishes between physical consumption and market-based transactions. This directly addresses the EU's concern regarding green certificates in CBAM, where unclear rights-responsibility relationships between GCs, green power usage, and carbon emissions create double-counting risks that undermine carbon border adjustment integrity.
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Abstract
The segmentation between China's green certificate (GC) trading and carbon markets has created a critical accounting gap: existing regional grid emission factors fail to deduct renewable energy environmental attributes already transferred via GC transactions, resulting in double counting of green power benefits. This study proposes a carbon emission accounting framework that integrates geospatial information with GC trading mechanisms. A GC-carbon reduction association model is established, systematically classifying GC transaction types and formulating corresponding accounting criteria. A differentiated accounting framework based on transaction characteristics enables spatial matching between GC transactions and regional power grids. Kalman filtering and inversion techniques are applied to optimize the accuracy of thermal power plant carbon emission monitoring data. Validation is conducted using the IEEE 30-bus system and a geographic region in China. The framework addresses the dual-calculation deficiency in current practices, where grid emission factors retain renewable energy shares that have already been traded as GCs. By establishing a mapping relationship between the power network and geographic regions, the method enables precise carbon accounting at both source and consumption ends. The results demonstrate the rationality and effectiveness of the proposed approach, providing a methodological foundation for coordinating GC trading with carbon market accounting rules and supporting the establishment of unified carbon emission accounting standards that avoid environmental benefit duplication.
1. Introduction
China's power sector faces a structural accounting failure: the current regional grid emission factor methodology does not deduct renewable energy consumption volumes whose environmental attributes have already been transferred through green certificate transactions. This creates a double-counting pathway where the same unit of green power claims zero-carbon status at the generation side while its environmental benefit is simultaneously claimed by certificate purchasers, effectively inflating the renewable energy contribution to grid emission factors. The consequence is a systematic overstatement of carbon reduction achievements and a distortion of carbon market price signals, as entities may claim emission reductions that have already been accounted for elsewhere in the system.
The proposed methodology directly intervenes at this accounting bottleneck by establishing a GC-carbon reduction association model that classifies transaction types and formulates differentiated accounting criteria. Kalman filtering and inversion techniques are applied to thermal power plant monitoring data to improve source-side measurement accuracy, while a spatial matching framework maps GC transactions onto regional power grids using the IEEE 30-bus system and a Chinese geographic region as validation platforms. This approach enables the separation of physical electricity consumption from market-based environmental attribute transfers, providing a replicable protocol for coordinating GC trading with carbon accounting rules and eliminating the environmental benefit duplication that currently undermines both China's carbon market integrity and international carbon border adjustment compatibility.
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MA Zhaoxing, LIU Chengshuang, XU Peng, CHEN Hao, WANG Ruihua (2026). Carbon Emission Accounting and Analysis Method Considering Green Certificate Trading and Grid-Region Mapping. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9714
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Frequently Asked Questions
What specific failure mechanism in current carbon accounting does the GC-carbon reduction association model address, and what is the quantitative impact of this failure?
The failure mechanism is environmental attribute double counting: China's regional grid emission factors retain the renewable energy share that has already been transferred through GC transactions, meaning the same green power's zero-carbon attribute is claimed both in the grid factor calculation and by the GC purchaser. This results in up to 100% overstatement of renewable energy carbon reduction benefits for affected transactions. The proposed model systematically classifies GC transaction types and establishes corresponding accounting criteria to deduct transferred environmental attributes from grid emission factors, eliminating this duplication.
How does the Kalman filtering and inversion technique improve thermal power plant carbon emission monitoring data, and what specific data quality issues does it resolve?
Thermal power plant carbon emission monitoring data contains measurement noise, systematic sensor errors, and temporal inconsistencies that propagate through the accounting chain. Kalman filtering provides optimal state estimation under Gaussian noise assumptions, while inversion techniques reconstruct unmeasured or corrupted data points from observable system outputs. This combination improves data accuracy for source-side carbon accounting, which is critical because fossil fuel combustion at power plants represents the primary carbon emission source in the power system. Enhanced data precision directly affects compliance verification and carbon trading settlement accuracy.
What is the scalability bottleneck for implementing the grid-region mapping framework, and how does the IEEE 30-bus validation address real-world deployment constraints?
The scalability bottleneck lies in the spatial resolution mismatch between GC transaction records (which may be aggregated at provincial or national levels) and physical grid topology (which operates at bus-level granularity). The IEEE 30-bus system provides a standardized testbed with defined node connectivity and power flow characteristics, enabling validation of the mapping algorithm under controlled conditions. The Chinese geographic region case extends this to real-world spatial data, demonstrating that the framework can handle the data volume and heterogeneity of actual provincial grids. Deployment constraints include data availability at bus-level resolution and the computational complexity of real-time spatial matching.
How does the proposed framework address the EU's concern regarding green certificates in CBAM, and what specific rights-responsibility relationships does it clarify?
The EU's CBAM does not accept green certificates as carbon deduction evidence because the rights-responsibility relationship between GCs, green power usage, and carbon emissions lacks clarity, creating double-counting risks. The proposed framework clarifies this by establishing a traceable chain: GC represents 1 MWh of green power environmental rights, the purchaser can claim the corresponding emission reduction, and the grid emission factor must deduct this transferred volume. This creates a one-time claim principle where environmental benefits are declared only by the buyer, not simultaneously in grid factors. The spatial matching between GC transactions and regional grids ensures that the deduction is applied to the correct geographic area, preventing cross-regional double counting.
What are the cost and data infrastructure requirements for implementing this accounting framework at provincial grid scale, and how do they compare to Japan's dual emission factor system?
Implementation requires: (1) bus-level or substation-level electricity consumption data with hourly or sub-hourly resolution; (2) GC transaction records with geographic identifiers and temporal stamps; (3) thermal power plant continuous emission monitoring systems (CEMS) data for Kalman filtering input; and (4) grid topology and power flow data for spatial matching. Japan's dual emission factor system (basic factor retaining environmental attributes and adjusted factor excluding them) provides a simpler alternative but lacks the spatial granularity for sub-provincial accounting. The proposed framework's computational cost is higher due to Kalman filtering and spatial matching algorithms, but it enables more precise accounting that supports localized carbon reduction policies and avoids the cross-subsidization inherent in provincial average factors.
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