Spatiotemporal characteristics and influencing factors of non-CO2 greenhouse gas emission intensity from China's livestock sector

Xiaodong Pei; Junhao Li; Lihua Zhou; Ya Wang; Gui Shi; Cong Zhang; Jian Yang

doi:10.1016/j.scitotenv.2024.178191

Spatiotemporal characteristics and influencing factors of non-CO₂ greenhouse gas emission intensity from China's livestock sector

Sci Total Environ. 2024 Dec 20:958:178191. doi: 10.1016/j.scitotenv.2024.178191. Online ahead of print.

Authors

Xiaodong Pei¹, Junhao Li², Lihua Zhou³, Ya Wang², Gui Shi¹, Cong Zhang¹, Jian Yang¹

Affiliations

¹ Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China.
² Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.
³ Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: lhzhou@casisd.cn.

PMID: 39708742
DOI: 10.1016/j.scitotenv.2024.178191

Abstract

The livestock sector is a major source of non-CO₂ greenhouse gas (GHG). As China has the world's largest livestock production, analyzing factors influencing GHG emission intensity of livestock (GEIL) is crucial for guiding its emission reduction policies. Yet, most current studies on livestock GHG focus on emission amount (GEAL) over GEIL, neglecting comprehensive utilization of spatial econometric models. Furthermore, the influencing factors mainly focus on population, economic level, urbanization, and industrial structure, while the exploration of factors such as technological innovation, consumers' age structure, and industrial agglomeration remains insufficient. This study first applied the standard deviation ellipse (SDE) model to describe the spatiotemporal evolution of GEAL and GEIL across 31 Chinese provinces from 2006 to 2022. Then, using extended STIRPAT theory, we analyzed GEIL's influencing factors with exploratory spatial data analysis (ESDA) and the geographically and temporally weighted regression (GTWR) model. The results showed that: (1) GEAL was concentrated in resource-rich grassland pastoral and grain-producing areas. Provincial GEAL trends varied, while national GEAL declined rapidly and then fluctuated slightly. Cattle, pigs, and sheep/goats contributed the most to GEAL. (2) GEIL significantly decreased nationwide and provincially, with Tibet having the highest reduction rate but still leading in GEIL. (3) GEAL followed a northeast-southwest distribution, whereas GEIL exhibited an east (slightly north) to west (slightly south) pattern. (4) Spatial analysis revealed significant GEIL clustering, with higher values in western provinces and lower in central and eastern regions. (5) Population scale, urbanization, age structure, proportion of ruminant animal production value, location quotient of livestock, employment income per capita of agricultural personnel, patent-granted intensity, and educational level in rural areas had diverse spatiotemporal impacts on GEIL across provinces. Tailored emission reduction policies and regional collaborative governance are recommended.

Keywords: Emission intensity; GTWR model; Greenhouse gas; Influencing factor; Livestock sector.