“…Two provinces, Hubei and Hunan, were identified as the high-risk provinces ( Liu et al, 2021 ), seven were medium-risk provinces, such as Jiangsu, Anhui and Sichuan, and the Xinjiang Uygur Autonomous Region was selected as a low-risk province. Because about two thirds of total meat consumption in China is pork, pork was selected as the livestock product to assess the provincial agricultural product price differences, ( Xie et al, 2020 ), crucian carp, carp, and silver carp were selected as the aquatic products, based on the agricultural product types in the China Agricultural Price Survey Yearbook, celery cabbage, cucumber, beans and green pepper were selected as the representative vegetables, and Fuji apples, watermelons and bananas were selected as the representative fruit. Then the average prices for four agricultural products in each risk area were obtained to determine the high-risk, medium-risk, and low-risk areas’ agricultural product price time series, which were denoted foodprice - h, foodprice - m, and foodprice - l. Data processing Fig.…”
“…Two provinces, Hubei and Hunan, were identified as the high-risk provinces ( Liu et al, 2021 ), seven were medium-risk provinces, such as Jiangsu, Anhui and Sichuan, and the Xinjiang Uygur Autonomous Region was selected as a low-risk province. Because about two thirds of total meat consumption in China is pork, pork was selected as the livestock product to assess the provincial agricultural product price differences, ( Xie et al, 2020 ), crucian carp, carp, and silver carp were selected as the aquatic products, based on the agricultural product types in the China Agricultural Price Survey Yearbook, celery cabbage, cucumber, beans and green pepper were selected as the representative vegetables, and Fuji apples, watermelons and bananas were selected as the representative fruit. Then the average prices for four agricultural products in each risk area were obtained to determine the high-risk, medium-risk, and low-risk areas’ agricultural product price time series, which were denoted foodprice - h, foodprice - m, and foodprice - l. Data processing Fig.…”
“…(2020) found that the method has an error of less than 20% at the provincial level in China. The method has the characteristics of low calculation cost and high reliability of results and has been widely used (Gao et al., 2020; Soligno, Malik & Lenzen, 2019, Soligno, Rodolfi & Laio, 2019; Tamea et al., 2020; Xie et al., 2020).…”
Section: Methods and Datamentioning
confidence: 99%
“…This will lead to the key information that the scarce water consumed locally comes from other export regions being hidden and ignored, and the stress on regional water resources will be underestimated or overestimated, thus affecting decision‐making. This issue matters because over 90 percent of the WF of livestock comes from producing feed crops (Mekonnen & Hoekstra, 2012; Xie et al., 2020). For instance, if water‐rich region A imports feed crops for livestock from water‐scarce region B, then pervious WS assessments would attribute WF of the livestock to region A’s water endowments, which would be misleading remote water scarcities within the value chain of animal products.…”
Section: Introductionmentioning
confidence: 99%
“…Xie et al. (2020) quantified the spatiotemporal evolution of the WF of pork at the provincial scale under different farming scales within China over the period 2000–2014. However, few studies have quantified the VW flows induced by the consumption of animal products and their intrinsic relationships with the WF and the VW flow related to feed crops.…”
Growth in water consumption with intensified virtual water (VW) flows from increased production and consumption of both meat and feed crops threatens the sustainability of water resources in water‐scarce countries and export regions. However, a sustainability assessment of both water footprints (WFs) and VW flows related to animal products at a subnational scale is lacking. Here we estimate direct and indirect WFs as well as the inter‐provincial VW flows associated with pork production and consumption in China for the years 2008 and 2017. The contributions of feed crop production and consumption were identified. Both life cycle assessment and WF network frameworks were applied to evaluate the sustainability of blue WF and VW flows. Results show that the national annual consumptive (green‐blue) WF and degradative (gray) WF of pork production increased by 8.7% and 15.8%, respectively. More than 80% of the blue WF in pork production was unsustainable. By 2017, 62% of the unsustainable blue WF and 64% of the water scarcity footprints of pork production in the south resulted from consuming the feed crops from the north. This analysis highlights the importance and provides feasible approaches to uncover remote geographical effects on regional water scarcities from different steps in the value chains of livestock products.
“…Efficiency of Pig Breeding Industry erefore, more and more scholars have introduced natural resources and environmental factors into the process of economic growth and put forward the green economic growth theory [8]. Combined with the sustainable development theory, the green economic growth theory is put forward.…”
Section: Environmental Regulation and Productionmentioning
The study aims to improve the economic income of pig breeding industry under environmental regulation and control the environmental pollution caused by pig breeding. Long short-term memory (LSTM) neural network combined with environmental regulation is proposed to forecast the price of live pigs, to reduce the cost of environmental pollution control and improve the production efficiency of pig breeding. Primarily, analyses are made on the industrial structure and pollution of pigs in China, and studies are carried out on the inevitability of large-scale and intensive pig breeding. Then, pig breeding and environmental pollution are coordinated under the environmental regulation. From the perspective of green total factor productivity, calculation is made on the profit of pig breeding and the cost of environmental pollution control. Next, the LSTM neural network is used to predict the price of live pigs, thus effectively controlling the scale of pig breeding and making timely decisions that conform to market rules. The results show that with the increase of feed and land prices, the advantages of large-scale pig breeding gradually become prominent, which leads to the small- and medium-sized scale farmers withdrawing from the market. Compared with other similar models, the designed model can better simulate the future trend of hog price, of which the prediction accuracy is over 80%. When combined with environmental regulations, the prediction accuracy of the model for different data sets reaches 83%, so the designed model can better predict the changing trend of the price of live pigs, thus improving the production efficiency of large-scale pig farmers.
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