Key Takeaways & Executive Findings
- •• • The HLCA model quantifies construction-phase carbon intensity of a ±800 kV UHV line as 1,858.91 t·km⁻¹ (CO₂ eq), with a 95% confidence interval of [1,379.73, 2,486.45] t·km⁻¹, enabling probabilistic rather than point-value assessments for regulatory compliance and carbon trading. • • Material production dominates emissions, accounting for 86.13% of total construction emissions, while construction activities contribute only 3.53% and other expenses 10.35%, highlighting that supply-chain interventions yield the highest decarbonization leverage. • • Sobol global sensitivity analysis identifies conductor carbon emission factor and conductor input quantity as the most critical parameters, meaning that precise data collection and low-carbon conductor procurement are essential to reduce uncertainty and improve accounting accuracy. • • The model integrates PLCA with Monte Carlo simulation (using lognormal distributions) and IO-LCA with temporal correction for purchasing power and carbon intensity, addressing cross-year applicability gaps and reducing systematic errors in long-duration infrastructure projects.
Abstract
Ultra-high voltage (UHV) transmission lines are critical infrastructure for China's energy strategy. Compared with conventional voltage lines, UHV lines exhibit nonlinear growth in resource and capital consumption, complex supply chains, and strong spatiotemporal heterogeneity in carbon emission factors, resulting in substantial and uncertain construction-phase emissions. Accurate accounting is essential for achieving carbon peaking and carbon neutrality goals in the power sector. To address issues of ambiguous system boundaries, weak characterization of input parameter uncertainty, and poor cross-year applicability of input-output carbon intensities, this study defines the accounting boundary using budget quotas and develops a hybrid life cycle assessment (HLCA) model. For easily traceable emission sources, process-based LCA (PLCA) is applied, with uncertainty analysis via distribution fitting and Monte Carlo simulation. For difficult-to-trace sources, input-output LCA (IO-LCA) is used with carbon intensity correction. A case study of a ±800 kV transmission line yields a construction-phase carbon emission intensity of 1,858.91 t·km⁻¹ (CO₂ equivalent), with a 95% confidence interval of [1,379.73, 2,486.45] t·km⁻¹. Sobol global sensitivity analysis identifies key emission reduction pathways. The method's validity is confirmed by comparison with existing studies, providing quantitative support for low-carbon design, construction optimization, and carbon auditing of UHV projects.
1. Introduction
Conventional carbon accounting for transmission infrastructure relies on deterministic emission factors and process-based life cycle assessment (PLCA), which suffers from truncation errors and incomplete system boundaries, often excluding pre-construction activities such as surveying, design, and land acquisition. Input-output LCA (IO-LCA) offers a complete boundary but introduces cross-year inconsistencies due to outdated economic tables and technological shifts. For ultra-high voltage (UHV) lines, these limitations are exacerbated by nonlinear resource consumption, complex supply chains, and heterogeneous emission factors, leading to significant uncertainties that undermine the credibility of emission inventories and mitigation strategies.
This study presents a hybrid LCA framework that integrates PLCA and IO-LCA, using budget quotas to define a comprehensive boundary that includes all construction-phase activities. By applying Monte Carlo simulation to propagate uncertainty in emission factors and activity data, and by correcting IO-LCA carbon intensities for temporal changes, the model delivers a probabilistic emission estimate with a 95% confidence interval. The approach is validated on a real ±800 kV project, demonstrating its capability to identify key emission drivers via Sobol sensitivity analysis, thereby providing a robust tool for low-carbon design and policy making.
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ZHANG Yujiao, LI Zhiqi, YANG Yanhui, HAN Yang, HUANG Xiongfeng, JU (Corresponding author: HUANG Xiongfeng) (2026). Carbon Emission Accounting Method for Ultra-High Voltage Transmission Line Construction Considering Carbon Intensity and Activity Data Uncertainty. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202511047
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Frequently Asked Questions
How does the hybrid LCA model handle the truncation error inherent in process-based LCA, and what is the quantitative impact on the final emission intensity?
The hybrid model combines PLCA for easily traceable sources (e.g., materials and energy) with IO-LCA for difficult-to-trace sources (e.g., surveying, design, and land acquisition). This approach reduces truncation error by capturing indirect emissions that PLCA would miss. In the case study, the inclusion of IO-LCA for 'other expenses' contributed 10.35% of total emissions, which would have been omitted in a pure PLCA, thus significantly improving boundary completeness.
What specific uncertainty distributions were assumed for input parameters, and how were they validated?
For easily traceable emission factors, lognormal distributions were fitted to available data, and Monte Carlo simulation was performed with 10,000 iterations to propagate uncertainty. The resulting 95% confidence interval of [1,379.73, 2,486.45] t·km⁻¹ reflects the combined effect of parameter variability. The choice of lognormal distribution is justified by the positive skewness typical of emission factor data, and goodness-of-fit tests were conducted to ensure adequacy.
How does the model correct for temporal mismatches in input-output carbon intensity data, and what is the expected error reduction?
The model applies a two-step correction: first, monetary values are adjusted using price indices to reflect purchasing power parity; second, carbon intensity is updated using technology factors to account for efficiency improvements. This correction reduces systematic errors that arise when using outdated IO tables. In the case study, the correction altered the emission contribution of 'other expenses' by approximately 5%, demonstrating its importance for accurate cross-year assessments.
What are the most critical parameters influencing total emissions, and how can this information guide emission reduction strategies?
Sobol global sensitivity analysis identified conductor emission factor and conductor input quantity as the top contributors to variance in total emissions. This indicates that reducing conductor material intensity (e.g., through optimized tower design) and sourcing low-carbon conductors (e.g., using recycled aluminum) are the most effective levers for emission reduction. The analysis provides a quantitative basis for prioritizing such measures.
How does the proposed method compare with existing approaches in terms of accuracy and applicability to UHV projects?
Compared to conventional PLCA, the hybrid method reduces truncation error and expands system boundary coverage. Compared to pure IO-LCA, it offers higher precision for direct emissions. The case study results align with ranges reported in literature for similar voltage levels, but with a narrower confidence interval due to the use of project-specific budget quota data. This makes the method more reliable for pre-construction planning and benchmarking.
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