Dynamic prediction and quantitative assessment of carbon emissions from animal husbandry: A case study of inner mongolia autonomous region, China.

Abstract

Climate change, driven by greenhouse gas emissions, has emerged as a pressing global ecological and environmental challenge. Our study is dedicated to exploring the various factors influencing greenhouse gas emissions from animal husbandry and predicting their future trends. To this end, we have analyzed data from China's Inner Mongolia Autonomous Region spanning from 1978 to 2022, aiming to estimate the carbon emissions associated with animal husbandry in the region. Furthermore, we have constructed an SA-STIRPAT model grounded in scenario analysis to forecast the timing of the carbon emissions peak. Our findings reveal several notable trends. From 2001 to 2022, carbon emissions from animal husbandry in the region followed a pattern of "rapid growth, followed by smooth fluctuations, and then a gradual recovery." Notably, in 2019, the region reached a peak contribution to China's animal husbandry carbon emissions, accounting for 8.34% of the national total. Ruminants, including cattle, sheep, and camels, were identified as the primary emitters, responsible for 91.6% of the total greenhouse gas emissions. Additionally, our study indicates that factors such as production efficiency, industrial structure, economic level, and population structure positively impact carbon emissions, while population size negatively affects animal husbandry's carbon footprint. Our model predicts that under both low-carbon and benchmark scenarios, carbon emissions from animal husbandry in the region are expected to decline after 2030. However, under a high-carbon scenario, emissions are anticipated to peak in 2040. In conclusion, to achieve Inner Mongolia's "dual carbon" goals, it is imperative to implement effective population control measures, enhance production efficiency, elevate the level of urbanization, and optimize the industrial structure.