Key Takeaways & Executive Findings
- •• • The proposed method reduces carbon footprint deviation from 0.05% to 3.03% and uncertainty from 2.65% to 5.45% as data missing deepens, demonstrating robust accuracy under incomplete data. • • Under mixed data missing scenarios, carbon footprint uncertainties for five electromechanical products (including transformers, high-speed diesel engines, wind turbines) remain below 10%, validating industrial applicability. • • Integration of data quality indicators and Monte Carlo simulation quantifies uncertainty, enabling identification of critical data inputs and improving reliability of carbon footprint results. • • The method addresses data gaps from suppliers, manufacturers, and databases via substitution algorithms and correction coefficients, overcoming limitations of existing single-product LCA studies.
Abstract
Addressing the insufficient applicability and accuracy of carbon footprint accounting for electromechanical products due to data gaps, this study proposes an accounting method based on life cycle assessment (LCA). Using LCA as the overall framework with a system boundary of "cradle to gate", the method constructs a carbon footprint accounting approach combining substitution algorithms and correction coefficients for different data missing scenarios from suppliers, manufacturers, and databases. Data quality indicators and Monte Carlo simulation are employed to quantify data quality and uncertainty, while single-factor and multi-factor sensitivity analyses identify key influencing factors. Taking an optical gyrocompass as a case study, the carbon footprints and uncertainties under eight typical data missing scenarios are explored, and robustness checks are conducted on five typical products including transformers, high-speed diesel engines, and wind turbines. As data missing degree deepens, the carbon footprint deviation of the optical gyrocompass increases from 0.05% to 3.03%, and uncertainty rises from 2.65% to 5.45%. Under mixed data missing scenarios, the carbon footprint uncertainties of all five electromechanical products remain below 10%. The method exhibits wide applicability, strong implementability, and high accuracy, effectively supporting carbon footprint accounting for electromechanical products, reducing carbon tariff risks, optimizing emission reduction strategies, and promoting green development.
1. Introduction
Electromechanical products are vital to modern industry, yet their carbon-intensive nature exposes them to escalating carbon tariff policies in developed economies, creating trade barriers and compliance risks. Existing carbon footprint accounting methods, primarily based on input-output analysis (IOA) or life cycle assessment (LCA), face critical bottlenecks: IOA lacks product-level detail, while LCA studies often focus on single products and fail to address the pervasive data missing scenarios in complex supply chains. Data gaps arise from suppliers lacking carbon accounting methods, manufacturers with limited data collection capabilities, and databases with incomplete coverage of diverse product types, undermining the accuracy and applicability of carbon footprint assessments.
This study introduces a novel LCA-based accounting method specifically designed to handle multiple data missing scenarios. By combining substitution algorithms and correction coefficients, the method systematically compensates for missing data from suppliers, manufacturers, and databases. It further integrates data quality indicators and Monte Carlo simulation to quantify uncertainty, and employs sensitivity analysis to identify key factors. The method's effectiveness is demonstrated through a case study on an optical gyrocompass and robustness checks on five typical electromechanical products, showing that carbon footprint uncertainties remain below 10% even under mixed data missing conditions. This approach provides a practical, accurate solution for carbon footprint accounting, supporting industries in mitigating carbon tariff risks and optimizing emission reduction strategies.
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WEI Xikai, RUAN Jiatong, TAN Xiaoshi (2026). Carbon Footprint Accounting Method for Electromechanical Products Based on Life Cycle Assessment. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202507046
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Frequently Asked Questions
How does the proposed method handle data missing scenarios from suppliers, manufacturers, and databases?
The method employs substitution algorithms and correction coefficients tailored to each data missing scenario. For supplier data gaps, it uses alternative data sources or estimation algorithms; for manufacturer data limitations, it applies correction factors to adjust aggregated data; for database gaps, it utilizes proxy emission factors with adjustments. This approach ensures accurate carbon footprint estimation even when primary data are incomplete.
What is the quantitative impact of data missing on carbon footprint accuracy and uncertainty?
In the optical gyrocompass case study, as data missing degree increases, carbon footprint deviation rises from 0.05% to 3.03%, and uncertainty increases from 2.65% to 5.45%. Under mixed data missing scenarios, uncertainties for five electromechanical products remain below 10%, indicating the method maintains acceptable accuracy and reliability.
How is uncertainty quantified and what role does it play in the accounting method?
Uncertainty is quantified using data quality indicators and Monte Carlo simulation. Data quality indicators assess the reliability of activity data and emission factors, while Monte Carlo simulation propagates these uncertainties through the carbon footprint calculation. This provides a confidence interval for the results, enabling stakeholders to understand the robustness of the carbon footprint values and prioritize data improvements.
Can the method be applied to different types of electromechanical products beyond the case study?
Yes, the method is designed to be generic and adaptable. Robustness checks on five diverse products (transformer, high-speed diesel engine, wind turbine, etc.) demonstrated that carbon footprint uncertainties remain below 10% under mixed data missing scenarios, confirming the method's wide applicability across various electromechanical products.
What are the key factors influencing carbon footprint results, and how are they identified?
Key factors are identified through single-factor and multi-factor sensitivity analyses. These analyses evaluate how variations in input parameters (e.g., material emission factors, energy consumption) affect the final carbon footprint. By ranking these factors, the method highlights which data require the most attention to improve accuracy, guiding data collection and quality enhancement efforts.
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