Optimization analysis of grain self-production and import structure based on carbon footprint

Abstract

Purpose

The purpose of this paper is to reduce the carbon footprint of food by adjusting the international trade and planting structure and to provide possible ideas for the improvement of the world's food green production and green trade.

Design/methodology/approach

Using the literature analysis method to collect carbon footprint data calculated based on the life cycle assessment (LCA) method, and establishing an optimization model and an ARIMA prediction model for empirical analysis, this paper explores the possibility to reduce carbon emissions by adjusting import structure and self-production structure.

Findings

The results show that only through the adjustment of the import structure, carbon emissions can be reduced by 3.29 million tons at the source of imports. When domestic self-production is included, a total of 4.51 million tons of carbon emissions can be reduced, this provides ideas for low-carbon emission reduction in agriculture and animal husbandry.

Originality/value

This article is the first to use the carbon footprint data obtained by other scholars using LCA to optimize and analyze the grain trade structure and planting structure from a low-carbon perspective, and obtain specific emission reductions.

Citation

Zhang, H., Zhao, F. and Han, K. (2022), "Optimization analysis of grain self-production and import structure based on carbon footprint", China Agricultural Economic Review, Vol. 14 No. 4, pp. 741-757. https://doi.org/10.1108/CAER-02-2022-0036