Assessing Carbon Emissions from Animal Husbandry in China: Trends, Regional Variations and Mitigation Strategies

Author:

Peng Cheng1,Wang Xiaona1,Xiong Xin2,Wang Yaxing3

Affiliation:

1. School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China

2. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

3. School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China

Abstract

The intensification of land use and the accelerated integration of three industries (agricultural production, agricultural product processing industry, and agricultural product market service industry) in China have resulted in the continuous expansion of animal husbandry and its industrial chain. This phenomenon has led to a rise in greenhouse gas (GHG) emissions from livestock farming, intensifying climate change and placing strain on worldwide environmental conservation efforts. Life cycle assessment (LCA) was utilized to evaluate carbon emissions from China’s animal husbandry sector from 2012 to 2021. Additionally, the logarithmic mean divisia index (LMDI) decomposition method was employed to examine and elucidate the influential impacts of five factors on carbon emissions. These factors included the efficiency of animal husbandry production, the structure of the agricultural industry, per capita agricultural production income, urbanization, and the total population. The results reveal the following: (1) From 2012 to 2021, China’s animal husbandry sector witnessed a marginal increase in cumulative carbon emissions from 287.74 million tons to 294.73 million tons, with an annual growth rate of 0.42%. (2) Emission contributions were categorized as follows: the production stage (149.61 million tons), the transportation stage (145.07 million tons), and the processing stage (0.05 million tons). (3) The primary factor contributing to the rise in carbon emissions from animal husbandry from 2012 to 2021 was the per capita agricultural production income factor (A3), alongside a notable impact from the total population factor (A5).

Funder

National Social Science Fund Training Program of Southwest University of Science and Technology

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Sichuan Province

Publisher

MDPI AG

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