Can we design food taxes to reduce agricultural greenhouse gas emissions in China? A perspective from animal protein intake of low-income populations

Abstract

Purpose

Studies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.

Design/methodology/approach

The authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.

Findings

The results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.

Originality/value

This study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.

Citation

Chen, J., Sun, D., Zhong, F., Ren, Y. and Li, L. (2024), "Can we design food taxes to reduce agricultural greenhouse gas emissions in China? A perspective from animal protein intake of low-income populations", China Agricultural Economic Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/CAER-05-2023-0130