Non-negligible large impact of potential forestation on livestock production in China

Abstract

Forestation projects have been identified as an important component of climate mitigation strategy to reduce greenhouse gas emissions worldwide. However, most previous studies ignore the impacts of potential forestation projects on livestock production, which is crucial to the livelihoods of local people. In this study, we identified potential forestation areas in China by integrating random forest regression model and LPJ-GUESS model. The impacts of potential forestation on carbon storage and pasture-based livestock production were then analyzed. The results showed that China has a potential forestation area of 43.2 million hectares, accounting for about 19.6 % of the country’s forest area as reported in the 9th National Forest Inventory. If all these regions are reforested, China’s forest cover will increase to 27.4 %. Furthermore, 1.58 Pg C of new above- and below-ground carbon would be sequestered, about an increase of 17.2 % of current forest carbon storage. However, the potential forestation may result in a significant negative impact on existing pastures and the amount of livestock. It can reduce 4.7 % of beef, and 0.8 % of mutton products from China’s livestock sector each year. These significant declines will result in a huge gap in China’s livestock products supply, posing a serious threat to food security and the livelihoods of many people. Our findings highlight that potential forestation projects should further consider a reasonable pasture protection strategy to balance the potential carbon sequestration and the socio-economic benefits of livestock production.

Keywords

Forestation potential / Carbon storage / Livestock production / Socio-economic costs

CRediT authorship contribution statement

Changjiang Wu: Writing – original draft, Software, Methodology, Formal analysis, Conceptualization. Xiaoping Liu: Supervision, Project administration, Funding acquisition, Conceptualization. Fengsong Pei: Writing – review & editing, Supervision, Conceptualization. Kangyao Liu: Validation, Formal analysis, Data curation. Yaotong Cai: Validation. Xiaocong Xu: Supervision, Funding acquisition. Haoming Zhuang: Software, Methodology. Yiling Cai: Validation, Data curation. Bingjie Li: Validation.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the National Science Fund for Distinguished Young Scholars (Grant No. 42225107), the Natural Science Foundation of Guangdong Province of China (Grant No. 2022A1515012207), and the National Natural Science Foundation of China (Grant No. 42401505).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100270.

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