Adjustment of Meat Consumption Structure under the Dual Goals of Food Security and Carbon Reduction in China

Abstract

With the progress of economic development and increase in income, there have been significant transformations in the food consumption patterns among Chinese residents. Grain consumption has declined while the consumption of livestock products such as meat, poultry, eggs, and milk has gradually risen. Moreover, noteworthy adjustments have been observed in the meat consumption structure itself, with a substantial increase in poultry consumption and a significant decrease in pork consumption as representative of red meat. This study collects data mainly from the “Brick Agricultural Database” and “China National Grain & Oils Information Center”. And then, we employ the China Agricultural Industry Model (CASM) to simulate the economic and ecological consequences of augmenting poultry consumption as a substitute for red meat intake. The research findings demonstrate that ensuring an adequate intake of livestock products for residents without any decline will stimulate the doubling of China’s poultry meat demand by 2035 compared to 2020 while replacing pork. This would reduce feed grain requirements by 50 million tons and achieve carbon emission reductions amounting to 82 million tons. If we consider the consumption of poultry consumption growth in conjunction with its substitution for other red meats such as pork, beef, and mutton, this will save approximately 20 million tons of feed grains and lead to a reduction of around 103 million tons in carbon emissions. In conclusion, promoting future increases in poultry consumption as a substitute for pork and other red meat will yield extensive economic andecological benefits contributing toward international food security goals, as well as global carbon reduction targets. Additionally, advocating for increasing poultry consumption will also reduce the risk of chronic diseases and malignant tumors; this will significantly improve the national health states.

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

“…Consequently, relying solely on recognition rate is deemed unreasonable for assessing diagnostic model performance. Drawing inspiration from references [1,18,22], this article adopts a multi-classification evaluation index based on the confusion matrix. The chosen classifier evaluation metrics include precision, recall, and F1 score for assessing the classification performance of minority class samples.…”

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