Accounting greenhouse gas emissions of food consumption between urban and rural residents in China: a whole production perspective

Abstract

Food consumption is necessary for human survival. On a global scale, the greenhouse gas (GHG) emission related to food consumption accounts for 19%–29% of the total GHG emission. China has the largest population in the world, which is experiencing a rapid development. Under the background of urbanization and the adjustment of the diet structure of Chinese residents, it is critical to mitigate the overall GHG emission caused by food consumption. This study aims to employ a single-region input-output (SRIO) model and a multi-regional input-output (MRIO) model to measure GHG emission generated from food consumption in China and compare the contributions of different industrial sectors, uncovering the differences between urban and rural residents and among different provinces (autonomous regions/municipalities), as well as identifying the driving forces of GHG emission from food consumption at a national level. The results indicate that the total GHG emission generated from food consumption in China tripled from 157 Mt CO₂e in 2002 to 452 Mt CO₂e in 2017. The fastest growing GHG emission is from the consumption of other processed food and meat products. Although GHG emissions from both urban and rural residents increased, the gap between them is increasing. Agriculture, processing and manufacture of food, manufacture of chemical and transportation, storage and post services sectors are key sectors inducing food consumption related GHG emissions. From a regional perspective, the top five emission provinces (autonomous regions/municipalities) include Shandong, Hubei, Guangdong, Zhejiang, and Jiangsu. Based on such results, policy recommendations are proposed to mitigate the overall GHG emission from food consumption.

Graphical abstract

Keywords

greenhouse gas (GHG) emission / food consumption / industry sectors / mitigation measures / urban governance

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71810107001, 72088101, and 7169024).

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