Carbon accounting of pig manure management with a focus on China – discrepancies and recommendations

Abstract

Livestock manure management is a significant source of greenhouse gas (GHG) emissions in China, a leading country in pig farming. A scientific assessment of the carbon footprint of pig farming systems could provide a basis for further reducing GHG emissions in the livestock sector. This study reviewed the different GHG accounting methods for pig manure management, including Tier 2, Tier 2 Mass Flow, and Tier 3, and evaluated the impact of their implementation through a case study of an intensive pig farm. The results revealed that the emissions estimated by the IPCC Tier 2 method were 48% higher than those estimated by the Tier 2 Mass Flow method and 77% higher than those estimated by the Tier 3 process simulation-based method. Tier 2 Mass Flow and Tier 3 process modelling approaches are suggested to be more suitable for farm-level GHG emissions accounting, as the former tracks mass flow along the process and the latter incorporates regional climate conditions and microbiological activities. To enhance the accuracy and comprehensiveness of carbon accounting for China's pig farming system, it is recommended to monitor key GHG emission estimation parameters in Tier 2 Mass Flow and Tier 3 models across diverse regional farms. Furthermore, implementing these methods, which integrate farm-level accounting with regional models, could contribute to a comprehensive, bottom-up inventory of GHG emissions for manure management.

Graphical abstract: Carbon accounting of pig manure management with a focus on China – discrepancies and recommendations

Supplementary files

Article information

Article type
Paper
Submitted
02 Aug 2025
Accepted
16 Jan 2026
First published
21 Jan 2026
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Adv., 2026, Advance Article

Carbon accounting of pig manure management with a focus on China – discrepancies and recommendations

L. Zhang, X. Hu, X. Zheng, C. Zhang, Q. Liu, Z. Chen, H. Liu, C. Wang and L. Wang, Environ. Sci.: Adv., 2026, Advance Article , DOI: 10.1039/D5VA00248F

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