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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Research on Carbon Emission Forecasting
in the Agricultural and Livestock Industry
- A Case Study of Sichuan Province, China
1
Economics School, Guangzhou City University of Technology, Guangzhou, China
Submission date: 2024-12-09
Final revision date: 2025-01-23
Acceptance date: 2025-02-16
Online publication date: 2025-04-16
Publication date: 2026-04-21
Corresponding author
Pol. J. Environ. Stud. 2026;35(2):2233-2246
KEYWORDS
TOPICS
ABSTRACT
China’s agriculture and animal husbandry industry has entered a phase of rapid development.
At the same time, the production process of agriculture and animal husbandry generates a large amount
of greenhouse gas emissions. This thesis focuses on predicting carbon emissions from agriculture and
animal husbandry, i.e., predicting carbon emissions in areas where cultivation and animal husbandry
coexist. This thesis assumes that the future carbon emissions from agriculture and animal husbandry
in Sichuan Province, China, are predictable. This thesis introduces the regression algorithm in machine
learning the carbon emissions of agriculture and animal husbandry, mainly RF (Random Forest
Regression Algorithm), and proposes that the combined model of carbon emissions of agriculture
and animal husbandry is the FA-ACO-RF model (Factor Analysis-Ant Colony Optimization-Random
Forest Regression Algorithm). Empirical evidence of carbon emission prediction in agriculture
and animal husbandry was conducted in the Sichuan Province of China as an example, and the results
showed that the prediction accuracy reached 99.19%. It is concluded that the FA-ACO-RF model can
effectively and accurately predict carbon emissions from agriculture and animal husbandry in Sichuan
Province.
CONFLICT OF INTEREST
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.
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