Method for calculating food consumption structure deviation and carbon footprint thereof

Abstract

The invention discloses a method for calculating food consumption structure deviation and carbon footprint thereof, and belongs to the technical field of food consumption and environmental influence. A food consumption deviation index (DPD) is designed, consumption deviations of residents in all areas on nine kinds of main food (including meat, aquatic products, dairy products, eggs, oils, vegetables, fruits, beans and grains) within a period of time are quantified, and carbon footprints of food consumption are quantified in combination with life cycle carbon footprint coefficients of different kinds of food. According to the method, the variation trend of the resident dietary structure and the spatial difference of carbon emission are systematically analyzed, and the variation rule of the resident dietary structure deviation and the carbon footprint in different areas is disclosed. By calculating the consumption deviation and the carbon footprint of various foods, a scientific basis is provided for evaluating the dietary quality and optimizing the dietary structure, and the method is suitable for practical application in the fields of dietary optimization, health education, carbon footprint research and the like.

Description

Technical Field

[0001] The present invention relates to the field of food consumption structure and environmental impact, and in particular to a method for calculating food consumption structure deviation and its carbon footprint. Background Art

[0002] With the rapid economic development and the improvement of living standards, the dietary structure of residents has changed significantly. However, the problem of unbalanced dietary structure has become increasingly prominent, including excessive consumption of some foods and insufficient intake of other foods. This dietary imbalance not only poses a threat to residents' health, but also significantly exacerbates environmental problems such as carbon emissions.

[0003] However, there is a clear gap between actual food consumption and dietary guidelines, which can be quantified by food consumption deviation. In addition, the carbon footprint analysis of the food life cycle shows that food production and consumption are important sources of greenhouse gas emissions, especially animal-derived foods. Studies have shown that optimizing dietary structure can improve residents' health and reduce carbon footprint at the same time.

[0004] Existing technologies mainly focus on the consumption of a single region or a specific food, and lack a method to systematically evaluate food consumption deviations and their carbon footprints. Therefore, it is necessary to develop a scientific and systematic method to quantify the deviations in food consumption structure and its carbon footprint of residents in each region, so as to provide data support for evaluating the quality of residents' diets and reducing carbon footprints. Summary of the invention

[0005] The present invention proposes a method for calculating the deviation of food consumption structure and its carbon footprint, systematically analyzing the changes in residents' dietary structure and its environmental impact, and solving the problems of limited analysis scope and insufficient system in the prior art. By analyzing the deviation of food consumption and its carbon footprint, the method reveals the current status of food consumption of residents in different regions and urban and rural areas, and provides a scientific basis for optimizing food consumption structure and reducing carbon footprint.

[0006] The core contents of the present invention include:

[0007] (1) Calculation of food consumption deviation. The food consumption deviation index (DPD) is designed to quantify the difference between residents’ actual food consumption and the recommended standard. The per capita consumption deviation of major foods by residents in each region over a period of time is calculated. The calculation of food consumption deviation is shown in formula (1):

[0008]

[0009] Where DPD ijt Indicates food consumption deviation (unit: kg cap -1 yr -1 ), reflecting the relative proportion of actual consumption of a certain type of food exceeding the recommended range. The larger the consumption deviation index, the worse the dietary structure.ijt represents the per capita consumption of food i by residents in region j in year t (unit: kg cap -1 yr -1 ). Recommended maximum intake of food i (X max,i ) and minimum intake (X min,i ) as a reference standard.

[0010] The total consumption deviation of all foods is shown in formula (2):

[0011]

[0012] Where DPD jt It represents the average consumption deviation of all nine kinds of food (including meat, aquatic products, eggs, dairy products, oil, fruit, vegetables, beans, and grains) in region j in year t. ijt The calculation method is shown in formula (1).

[0013] (2) Calculation of the carbon footprint of food consumption. Based on the life cycle carbon footprint coefficient, the carbon footprint of food consumption in each region was quantified. Monte Carlo simulation was used to analyze data uncertainty and the carbon footprint of total food consumption in each region was calculated comprehensively. The carbon footprint of food consumption per capita per year is C Total (kgCO2e cap -1 yr -1 ) The calculation method is shown in formula (3):

[0014]

[0015] Where, X ijt represents the per capita consumption of food i by residents in region j in year t (kg cap -1 yr -1 ). CF ijt represents the life cycle carbon footprint coefficient of food i.

[0016] (3) By analyzing the spatiotemporal distribution of food consumption deviation and carbon footprint, the current food consumption status of residents in different regions and its impact on carbon footprint are revealed.

[0017] Beneficial effects of the present invention:

[0018] (1) The present invention comprehensively covers residents in various regions and nine major types of food, and the analysis content is systematic and scientific.

[0019] (2) The present invention calculates food consumption deviation and carbon footprint, reveals the changing pattern of residents' food consumption structure and the temporal and spatial distribution of carbon footprint, and provides a quantitative basis for evaluating dietary quality and optimizing dietary structure. This method can be used in many fields such as dietary structure optimization, health education and carbon footprint research. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the calculation process of food consumption structure deviation and its carbon footprint. DETAILED DESCRIPTION

[0021] The specific implementation scheme of the present invention is further explained in combination with the technical scheme and the accompanying drawings.

[0022] This paper collects data on residents' consumption of nine major types of food (grain, vegetables, fruits, vegetable oils, beans, meat, aquatic products, dairy products, and eggs. Among them, grain, vegetables, fruits, and beans are plant-derived foods; meat, aquatic products, dairy products, and eggs are animal-derived foods). In order to solve the problem of missing data, we use linear interpolation to supplement the missing data.

[0023] According to formulas (1)-(3), the consumption deviation of each food is calculated, and the total food consumption deviation and classification deviation (animal-based food consumption deviation, plant-based food consumption deviation) are further calculated, and the total food consumption deviation of residents in each region is comprehensively calculated.

[0024] The life cycle carbon footprint coefficients of various foods are derived from relevant literature, and the parameters are defined as triangular probability distribution. The per capita carbon footprint of each food and the total per capita carbon footprint of the nine foods are calculated according to formula (3), and the per capita carbon footprint and total carbon footprint of each region are calculated comprehensively. The Monte Carlo method is used to simulate the uncertainty of carbon footprint, and the sampling is repeated 10,000 times.

[0025] The results showed that the overall deviation of the food consumption structure of residents in all regions from the actual food consumption of residents and the recommended standard (CDR) was serious, reaching 44-47%, but it showed a downward trend overall. The deviation of animal product consumption (58-62%) was always higher than the deviation of plant-based food consumption (24-32%). The consumption of vegetables, beans, fruits, eggs, dairy products, and aquatic products by residents did not meet the dietary standards, there was excessive consumption of meat, and residents in some areas over-consumed grain. The regional heterogeneity of the deviation of different types of food consumption was quite obvious. Among plant-based foods, the consumption deviations of regions F and A were the largest, at 78% and 65%, respectively. For vegetable and bean consumption, region F produced 52% and 65% consumption deviations, respectively, while the values ​​in region A were 25% and 55%, respectively. The deviation of grain consumption in region F was much higher than that in other regions, reaching 92%. The deviation of plant-based food consumption in region C was the smallest, at only 17%, and the consumption of plant-based food was very balanced. For animal-source food, the consumption deviation in region F is still the largest among all regions, reaching 81%, 11% higher than region E (70%), which has the second largest deviation in animal-source food consumption, and the degree of deviation from the standard is relatively serious. The average deviation of meat consumption in region E is the largest (69%), far exceeding the average meat consumption deviation of all regions (33%); dairy products are underconsumed in all regions, and due to natural conditions, the dairy consumption deviation in region E is the largest (96%); the deviation of aquatic product consumption in region F reaches 93%, far exceeding the average level of all regions; in addition, the deviation of aquatic product consumption in region A is also large, exceeding the CDR standard by 50%.

[0026] The total per capita carbon footprint of food consumption in all regions is 1.3tCO2e cap -1 Except for region F, the per capita food carbon footprint of other regions is relatively similar, ranging from 1.16-1.38tCO2e cap -1 The per capita carbon footprint of region F is 1.83tCO2ecap -1 This reflects that in the dietary structure of this region, animal-source foods with large carbon emission coefficients such as beef and pork account for a high proportion, resulting in a large carbon footprint, so the dietary structure needs to be adjusted and optimized urgently. The total carbon footprint of all regions is 1826MtCO2e, and the total carbon footprint of region D is 612MtCO2e, which contributes the most to the total carbon footprint of all regions, reaching 34%, and is a carbon emission reduction area that needs to be focused on.

[0027] The food consumption deviations and carbon footprints of the various regions involved in the present invention are shown in Table 1.

[0028] Table 1 Per capita food consumption deviation and per capita carbon footprint of each province involved in this invention

[0029]

Claims

1. A method for calculating food consumption structure deviation and its carbon footprint, characterized in that: Here are the steps: (1) Calculation of food consumption deviation: The food consumption deviation index (DPD) was designed to quantify the difference between residents’ actual food consumption and the recommended standard, and to calculate the per capita food consumption deviation of residents in various regions over a period of time. The formula for calculating food consumption deviation is as follows: Among them, DPD ijt Indicates food consumption deviation, unit: kg cap -1 yr -1 , reflecting the relative proportion of the actual consumption of a certain type of food exceeding the recommended range; the larger the consumption deviation index, the worse the dietary structure; X ijt represents the per capita consumption of food i by residents in region j in year t, unit: kg cap -1 yr -1 ; Recommended maximum intake of food iX max,i and minimum intake X min,i As a reference standard; The total consumption deviation of all foods is shown in formula (2): Where DPD jt represents the average consumption deviation of all nine kinds of food in region j in year t, including meat, aquatic products, eggs, dairy products, oils, fruits, vegetables, beans, and grains; (2) Calculate the carbon footprint of food consumption: Based on the carbon footprint coefficient of the food life cycle, quantify the carbon footprint of each type of food consumption in each region, use Monte Carlo simulation to perform data uncertainty analysis, and comprehensively calculate the carbon footprint of total food consumption in each region; the carbon footprint of per capita annual food consumption C Total The calculation method is shown in formula (3): Among them, X ijt represents the per capita consumption of food i by residents in region j in year t, unit: kg cap -1 yr -1 CF ijt represents the life cycle carbon footprint coefficient of food i; (3) By analyzing the spatiotemporal distribution of food consumption deviation and carbon footprint, the current food consumption status of residents in different regions and its impact on carbon footprint are revealed.

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