Spatiotemporal differentiation and trend prediction of carbon emissions in China’s swine industry

Abstract

Assessing carbon emissions in China’s swine industry and understanding its spatial–temporal characteristics under carbon peak and carbon neutrality goals are vital for promoting low-carbon farming. This study measures the swine industry’s carbon emissions across 30 provinces in China from 2006 to 2022 using life cycle assessment and IPCC coefficient methods. Spatial and temporal patterns were analyzed with exploratory spatial data analysis, and an XGBoost model was used to predict emissions from 2023 to 2032. The results show a “W”-shaped oscillating trend in total carbon emissions, with phases of rapid decline, fluctuating rise, fluctuating decline, and rapid resurgence. Emissions were highest in the Central region, followed by the West, East, and Northeast. Manure management and Feed crop cultivation were the primary emission sources, accounting for 59.7% and 29.9% of total emissions, respectively. The spatial pattern of high and low emission regions remained stable, with dynamic changes in moderate emission regions. High emissions were concentrated in the major grain-producing and livestock-raising and the South China coastal farming regions. The XGBoost model projects that China’s green development measures in the swine industry will have a significant future impact. The carbon emission intensity shows a trend of initial decline followed by stabilization, whereas total carbon emissions are projected to remain high and gradually increase, primarily due to continuous growth in production capacity. The conclusions of this study provide a reference basis for the green and low-carbon transformation of the swine industry and the optimization of the regional layout of the livestock industry.

Keywords

• Carbon emissions
• Life cycle assessment
• Machine learning
• Spatiotemporal characteristics
• Spatial correlation
• Swine industry