Uncertainty Analysis of Provincial Carbon Emission Inventories: A Comparative Assessment of Emission Factors Sources

Enhancing the precision of carbon accounting not only improves climate policy design, but also contributes directly to sustainability goals by enabling more targeted and accountable emission reduction strategies. Therefore, accurate carbon inventories are foundational to evidence-based climate action and sustainable development planning. This study estimates the carbon emissions of Hunan Province from 2016 to 2020 using the sectoral approach and energy activity data across four major sectors—industrial production, thermal power generation, transportation, and residential life. Emission factors (EFs) were drawn from three different sources: direct measurements, IPCC (Intergovernmental Panel on Climate Change) default values, and published literature. An improved Monte Carlo simulation method was employed to assess the uncertainty of carbon emission accounting associated with different EF sources. The experimental results indicated that carbon emissions calculated based on the literature and default EFs were systematically higher than those derived from empirical measurements, primarily due to discrepancies in the industrial and power generation sectors. In a representative year (2017), the carbon emission estimated based on measured EFs produced the narrowest confidence intervals, reflecting lower uncertainty (−5.31–8.17%), while the uncertainties of carbon emissions calculated using the literature and default EFs were −6.88–9.03% and −5.77–9.94%, respectively. The industrial carbon emissions were the dominant source of overall uncertainty, while the transportation carbon emission had a comparatively minor impact. Importantly, across all departments, the use of measured EFs significantly reduced the uncertainty of carbon inventories, reinforcing the value of locally calibrated data. These findings underscore the urgent need for improved EF measurement systems and standardized accounting practices to support the reliability of subnational carbon inventories.