Environmental load estimation device, environmental load estimation method, and program

Abstract

This environmental impact estimation device for estimating the environmental impact of a food product is provided with: a consumed food product weight estimation unit which estimates consumed weight of the food product using sales data of the food product and price data of the food product; and an environmental impact estimation unit which estimates the environmental impact of the food product using the consumed weight estimated by the consumed food product weight estimation unit and the environmental impact coefficient of the food product.

Description

[Technical field]

[0001] The present invention relates to a technique for estimating the environmental load caused by food. [Background technology]

[0002] First, the background art will be described. Note that documents referred to in this specification are indicated with "[1]" and the corresponding documents are listed at the end of the specification.

[0003] As of 2019, the global food supply chain emitted approximately 13.7 billion metric tons of carbon dioxide equivalent, accounting for 26% of anthropogenic greenhouse gas emissions. The food supply chain is highly resource intensive and covers approximately 43% of the ice- and desert-free land area[1] (Non-Patent Document 1).

[0004] Global food production is the world's largest user of water, with an estimated two-thirds of current freshwater withdrawals going to agricultural irrigation[1,2], and global carbon dioxide emissions must begin to decline significantly by 2030 to limit warming to 1.5°C.

[0005] Climate change is projected to affect the quality and quantity of food produced in the world and the capacity of those who distribute it. Food systems will also continue to face increasing pressures in the future due to global population growth. However, limited resources face constraints on the capacity to provide new land, fisheries, and freshwater to meet growing demand [4,5]. Modern agriculture also leads to environmental degradation through eutrophication, acidification, and soil degradation [6]. For the global food system to develop sustainably, it is necessary to consider and manage the impact of agriculture on environmental degradation and global warming. It is therefore important to estimate the environmental burden of food. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Poore, Joseph, and Thomas Nemecek. "Reducing food's environmental impacts through producers and consumers." Science 360.6392 (2018): 987-992. Summary of the Invention [Problem to be solved by the invention]

[0007] However, previous techniques for estimating the environmental burden of food rely on static consumption estimates rather than actual consumption data, for example from nutritional requirements[7], dietary guidelines[8], national food supply data[9] or data from FAOSTAT statistical databases

[10] . These databases and guidelines are updated less than once a year and do not reflect changes in consumption patterns based on changing seasons, natural disasters, pandemics or social trends.

[0008] Conventional estimates of the environmental burden of food are therefore not precise enough to track day-to-day changes in society and the food system, nor are they precise enough to track progress towards climate change mitigation and the 2050 net-zero emissions goal.

[0009] The present invention has been made in consideration of the above points, and aims to provide a technology that makes it possible to estimate the environmental impact of food that reflects actual consumption data at shorter time intervals than conventional technologies. [Means for solving the problem]

[0010] According to the disclosed technology, there is provided an environmental impact estimation device for estimating the environmental impact of food, comprising: Obtained from POS data The food 1 day Sales Amount data per unit weight of the food Price data and , divided by the market share of POS data multiplied by A food consumption weight estimation unit that estimates a consumption weight of the food; The food consumption weight estimated by the food consumption weight estimation unit To, Environmental load factor of the food By multiplying an environmental load estimation unit for estimating the environmental load of the food; An environmental load estimation device comprising: Effect of the Invention

[0011] The disclosed technology makes it possible to estimate the environmental impact of food that reflects actual consumption data at shorter time intervals than conventional technology. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram for explaining environmental load estimation in the prior art. [Diagram 2] FIG. 1 is a diagram showing an overview of an embodiment of the present invention. [Diagram 3] FIG. 1 is a configuration diagram of an environmental load estimation device 100. [Figure 4] FIG. 2 is a diagram showing an example of data in a meta-analysis DB 130. [Diagram 5] 3 is a flowchart of the process of the environmental load estimation device 100. [Figure 6] FIG. 2 illustrates an example of a hardware configuration of the apparatus. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, an embodiment of the present invention (the present embodiment) will be described with reference to the drawings. The embodiment described below is merely an example, and the embodiment to which the present invention is applied is not limited to the following embodiment.

[0014] (Environmental Impact of Food Systems) First, the environmental load of the food system that is the subject of this embodiment will be described. Note that the "food system" may also be called the "food system." Furthermore, the "environmental load of the food system" may also be called the "environmental load of food."

[0015] Regarding the environmental impact of the food system, studies have been conducted in various regions using various environmental impact indicators based on LCA (Life Cycle Assessment), such as energy consumption, land use, water withdrawal, fertilizer use, greenhouse gas (GHG) emissions, ozone creation, eutrophication, human toxicity, terrestrial acidification, particulate matter, ecotoxicity, and water pollution [11-14].

[0016] To calculate the environmental impact of a particular food, data on environmental indicators from multiple scientific studies are compiled and statistically analyzed, a process called meta-analysis. Meta-analysis is an important tool in environmental impact assessment, as it provides insight into the variations between production methods in different regions.

[0017] However, estimates of environmental burdens may vary because of subjective study assumptions and methods. Estimates of environmental burdens in meta-analyses are presented with uncertainty to reflect this variability.

[0018] In 2019, Poore and Nemecek published a comprehensive meta-analysis on the environmental impact of food systems [1]. The study compiled environmental impact data from 38,700 farms and 1,600 processors in 119 countries. [1] is the largest meta-analysis published on food systems.

[0019] The environmental impact indicators used in this meta-analysis are GHG emissions (kg CO2 equivalent), land use (m 2 years), acidification (gSO2 equivalent), eutrophication (gPO4 3-The data are the total amount of water withdrawals (in kiloliters equivalent) and scarcity-weighted water withdrawals (in kiloliters equivalent). The meta-analysis data from this study are publicly available. The data are geographically and statistically extensive, making them a suitable source of data for estimating the environmental burden of food consumption in Japan.

[0020] (Outline of this embodiment) Next, an outline of the present embodiment will be described. In this embodiment, an environmental load estimation device 100, which will be described later, estimates the environmental load of food at shorter time intervals than in the prior art.

[0021] For comparison, an outline of the environmental load estimation method according to the prior art is shown in Fig. 1, and an outline of the environmental load estimation method according to this embodiment is shown in Fig. 2. In Fig. 1 and Fig. 2, M indicates the weight of food consumed, and I indicates the environmental load of a certain index.

[0022] Conventional technology estimates the environmental impact of food using national survey data. Figure 1 shows an image of this. The update frequency is less than once a year, and as a result, the time resolution of the environmental impact estimates is less than once a year. In addition, with conventional technology, there is a delay of several years before the data is made public.

[0023] In addition, the data from the national survey is based on food supply data, and actual food consumption data is limited. It is also difficult to understand the impact of climate change, disasters, etc. on consumption.

[0024] On the other hand, in the environmental load estimation method of this embodiment, as shown in FIG. 2, the total weight of food consumed in one day (food consumption weight M) is estimated using POS data indicating total food sales and food price data that is updated approximately once a week.

[0025] Next, we estimate the environmental impact for a given food consumption weight using a meta-analysis database that shows environmental impact statistics for various food groups by weight, such as the database created by Poore and Nemecek [1]. The specific estimation method will be described later.

[0026] According to the environmental load estimation method of this embodiment, environmental load indicators such as land use, greenhouse gas emissions, and water intake can be calculated with high reliability at short time intervals.

[0027] Understanding the environmental burden of current consumption patterns is increasingly important for sustainable development, and understanding land use for food around the world allows us to calculate sustainable consumption patterns based on available resources.

[0028] For example, if we were to attempt to localize food systems in the future, sustainable consumption patterns in Japan, where land is scarce but water is abundant, would be different to sustainable consumption patterns in Australia, where land is abundant but water is limited.

[0029] Population growth projections can also be used to predict whether current consumption patterns are sustainable within the planet's limited resources. For example, consumption of food groups with the greatest environmental burden can be identified and replaced with nutritionally equivalent, more sustainable alternatives. Real-time calculations of environmental burdens also provide policymakers with a tool to assess progress towards environmental burden reduction targets for climate change mitigation.

[0030] (Example) The configuration and operation of the environmental load estimation device 100 will be described in detail below as an embodiment.

[0031] 3 shows a configuration diagram of the environmental load estimation device 100 in this embodiment. As shown in Fig. 3, the environmental load estimation device 100 has a POS-DB (database) 110, a food price DB 120, a meta-analysis DB 130, a data acquisition unit 140, a food consumption weight estimation unit 150, and an environmental load estimation unit 160.

[0032] The environmental load estimation device 100 may be configured by one device (computer) or by multiple devices. The environmental load estimation device 100 may be provided on a cloud. The POS-DB (database) 110, the food price DB 120, and the meta-analysis DB 130 may each be external to the environmental load estimation device 100. Each part will be described below.

[0033] <pos-db110> The POS-DB 110 stores POS data acquired from an external source, or may be a DB of a POS service provider, which will be described below, that is provided outside the environmental load estimation device 100.

[0034] The POS-DB 110 stores information on daily product sales, etc. Regarding the data in the POS-DB 110, any POS data may be used as long as it provides information on daily product sales, etc., but in this embodiment, as an example, data provided by the "real shopper SM" service

[15] by Shopper Insight is used. The "real shopper SM" service makes it possible to obtain daily sales data (POS data) on fresh foods without JAN codes, such as fresh meat, fish, fruits, and vegetables.

[0035] It is also possible to extend the environmental impact estimates for fresh foods to processed foods by using a weight equivalence coefficient to convert the weight of processed foods to the equivalent weight of fresh foods.

[0036] <Meta-analysis DB130> According to the literature [1], GHG (greenhouse gas) emissions (kg CO2 equivalent), land use (m 2 years), acidification (gSO2 equivalent), eutrophication (gPO4 3- Environmental impact data for 40 food groups based on water withdrawals (calculated as kL) and scarcity-weighted water withdrawals (calculated as kL) are publicly available. The Meta-Analysis DB130 contains data based on the literature [1].

[0037] FIG. 4 shows an example of data published by the literature [1], that is, data stored in the meta-analysis DB 130. The data shown in FIG. 4 is calculated by multiplying the productive area by the number of years of occupation (m 2 The table shows a sample of statistical coefficients for land use of various food groups, defined as the environmental impact coefficients (EIA). These coefficients are called environmental impact coefficients.

[0038] More specifically, the data shown in Figure 4 is a sample of land use data for 10 of the 40 food groups. Values ​​are m 2 / functional unit, where the functional unit is 1 kg of food crop.

[0039] <Food Price DB120> Data on food prices published by, for example, the Ministry of Agriculture, Forestry and Fisheries is stored in the food price DB 120. The data in the food price DB 120 is updated, for example, weekly (once a week).

[0040] <Data Acquisition Unit 140> The data acquiring unit 140 corresponds to the API shown in Fig. 2. The data acquiring unit 140 acquires POS data from the POS-DB 110, acquires food price data from the food price DB 120, and passes these data to the food consumption weight estimating unit 150.

[0041] Moreover, the data acquisition unit 140 acquires the environmental load coefficient from the meta-analysis D130B, and passes the acquired environmental load coefficient to the environmental load estimation unit 160.

[0042] <Food Consumption Weight Estimation Division 150> The food consumption weight estimation unit 150 estimates the food consumption weight by using the POS data and the food price data. Specifically, the food consumption weight estimation unit 150 estimates the food consumption weight M k is calculated using the following formula 1.

[0043] M k =s / pr (Equation 1) Here, s is the total daily sales (total food sales) [yen] for a particular food group obtained from POS data (

[15] ), r is the market share of the POS data (1.8% in the Shopper Insight database), and p is the price of the food per kilogram [yen / kg]. The Japanese Ministry of Agriculture, Forestry and Fisheries publishes data on average food prices on a weekly basis

[16] , and this data is used in this example. Note that the daily unit for the total sales obtained as POS data is just an example, and it may be hourly or weekly, for example.

[0044] <Environmental Impact Estimation Division 160> The environmental load estimation unit 160 uses the food consumption weight estimated by the food consumption weight estimation unit 150 and the environmental load coefficient obtained from the meta-analysis DB 130 to calculate an estimate of the environmental load of a certain weight of food.

[0045] Specifically, the environmental load estimation unit 160 estimates the environmental load index I n The total amount of environmental impact per day for all K food groups is calculated using the following formula 2. Note that the calculation period is one day only, and may be shorter than one day.

[0046] I n =Σ K k=0 C n_k M k (Formula 2) Here, C n_k is the environmental load factor of index n for food group k, and M k is the weight of food from food group k consumed on a given day.

[0047] Assume that the consumption weights of tomatoes, cheese, and beef on a certain day are 500t, 80t, and 200t, respectively. Using the data in Figure 4, we can calculate the amount of tomatoes (L tom_med ), Cheese (L cheese_med ), Beef (L beef_med ) the median land use is calculated as follows:

[0048] L tom_med =0.2×500×10 3 =0.1×10 6 [m 2 year] L cheese_med =20.2×80×10 3 =1.6×10 6 [m 2 year] L beef_med =170.4×200×10 3 =34×10 6 [m 2 year] Additionally, the range between the 5th and 95th percentiles can be used to calculate the range of land uses with a 90% confidence interval, as shown below.

[0049] L tom =(0.1~2.8)×10 6 [m 2 year](90% confidence level) L cheese =(0.6~25.9)×10 6 [m 2 year](90% confidence level) L beef =(10.0~182.0)×10 6 [m 2 year](90% confidence level).

[0050] The environmental impact of land use based on daily consumption of all fresh foods can be estimated by summing the estimated environmental impacts of land use for all food groups in the database, as described above. In addition, the annual land use associated with food consumption can be estimated by summing the environmental impacts of daily land use over a year.

[0051] The environmental impact estimation method in this embodiment reflects actual consumption patterns and incorporates food waste, making it possible to estimate the environmental impact more accurately than the conventional method that uses estimates of per capita calorie consumption[8].

[0052] In this embodiment, fresh foods are the subject of the estimation, but as described above, it is also possible to estimate the environmental impact of processed foods by using weight conversion coefficients.

[0053] <Operation flow> Referring to the flow chart of FIG. 5, a certain index I n The operation procedure of the environmental load estimation device 100 when calculating the environmental load for will be described.

[0054] In S1, the food consumption weight estimation unit 150 initializes k (the index of the food group) to 0. In S2, the food consumption weight estimation unit 150 calculates the total sales data s k is obtained from POS-DB110, and food price data p k is obtained from the food price DB120, and the food consumption weight M k Calculate the calculated M k is passed to the environmental load estimation unit 160.

[0055] If k=0, then I n = 0 and proceed to S4 (Yes in S3, S5). In S4, the environmental load estimation unit 160 obtains the environmental load coefficient C n_k Based on Equation 2, I n =I n +C n_k M k By I n Update.

[0056] The above process is repeated (S6, S7) while incrementing k by 1 until k=K, and in S8, an estimate of the environmental load for all of the K food groups is obtained. The environmental load estimation unit 160 may store the estimate of the environmental load in a storage device or may output it to the outside.

[0057] (Hardware configuration example) The environmental load estimation device 100 can be realized, for example, by causing a computer to execute a program. This computer may be a physical computer or a virtual machine on the cloud.

[0058] That is, the environmental load estimation device 100 can be realized by using hardware resources such as a CPU and memory built into a computer to execute a program corresponding to the processing performed by the environmental load estimation device 100. The program can be recorded on a computer-readable recording medium (such as a portable memory) and stored or distributed. The program can also be provided via a network such as the Internet or email.

[0059] Fig. 6 is a diagram showing an example of the hardware configuration of the computer. The computer in Fig. 6 has a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a CPU 1004, an interface device 1005, a display device 1006, an input device 1007, an output device 1008, and the like, all of which are connected to one another via a bus BS. Note that some of these devices may not be included. For example, if no display is performed, the display device 1006 may not be included.

[0060] The program for realizing the processing on the computer is provided by a recording medium 1001 such as a CD-ROM or a memory card. When the recording medium 1001 storing the program is set in the drive device 1000, the program is installed from the recording medium 1001 to the auxiliary storage device 1002 via the drive device 1000. However, the program does not necessarily have to be installed from the recording medium 1001, but may be downloaded from another computer via a network. The auxiliary storage device 1002 stores the installed program as well as necessary files, data, etc.

[0061] The memory device 1003 reads out and stores the program from the auxiliary storage device 1002 when an instruction to start the program is received. The CPU 1004 realizes the functions related to the device in accordance with the program stored in the memory device 1003. The interface device 1005 is used as an interface for connecting to a network. The display device 1006 displays a GUI (Graphical User Interface) or the like according to a program. The input device 1007 is composed of a keyboard and mouse, buttons, a touch panel, or the like, and is used to input various operation instructions. The output device 1008 outputs the results of calculations.

[0062] (Effects of the embodiment) As described above, the environmental load estimation device 100 according to this embodiment makes it possible to estimate the environmental load of food that reflects actual consumption data at shorter time intervals than the conventional techniques. More specifically, this is as follows.

[0063] The environmental load estimation device 100 according to this embodiment improves the time precision of environmental load estimation from once a year to once a day, making it possible to grasp dynamic changes in the environmental load of foods.

[0064] The environmental load estimation device 100 according to this embodiment can estimate the load on the environment by reflecting changes in consumption through indicators of environmental load such as annual land use, greenhouse gas emissions, acidification, eutrophication, and scarcity-weighted water withdrawal.

[0065] In addition, while conventional methods for estimating environmental impacts are very time-consuming, the technology according to the present embodiment allows environmental impacts to be estimated automatically in real time. Estimating the environmental impacts of food in real time provides a valuable tool for policymakers to manage progress towards environmental impact reduction targets. It is also possible to identify sustainable consumption patterns based on the resources available in different regions.

[0066] (Summary of the embodiment) This specification discloses at least the following environmental load estimation device, environmental load estimation method, and program: (Section 1) An environmental impact estimation device for estimating the environmental impact of food, comprising: a food consumption weight estimation unit that estimates a consumption weight of the food by using sales data and price data of the food; an environmental load estimation unit that estimates an environmental load of the food by using the consumption weight estimated by the food consumption weight estimation unit and an environmental load coefficient of the food; An environmental impact estimation device equipped with: (Section 2) The environmental load estimation unit obtains the environmental load coefficient from a meta-analysis database that stores the environmental load coefficient obtained by a meta-analysis of each food. 2. An environmental load estimation device according to claim 1. (Section 3) The food consumption weight estimation unit acquires the sales data from a database that stores POS data. 3. An environmental load estimation device according to claim 1 or 2. (Section 4) The environmental load estimation unit calculates a total amount of environmental load for a plurality of foods by adding up values ​​obtained by multiplying the consumption weight by the environmental load coefficient for the plurality of foods. 4. An environmental load estimation device according to any one of claims 1 to 3. (Section 5) An environmental load estimation method implemented by an environmental load estimation device that estimates environmental loads of food, comprising: a food consumption weight estimation step of estimating a consumption weight of the food by using sales data and price data of the food; an environmental load estimation step of estimating an environmental load of the food by using the consumption weight estimated in the food consumption weight estimation step and an environmental load coefficient of the food; A method for estimating environmental impacts. (Section 6) A program for causing a computer to function as each unit in the environmental load estimation device according to any one of claims 1 to 4.

[0067] Although the present embodiment has been described above, the present invention is not limited to such a specific embodiment, and various modifications and variations are possible within the scope of the gist of the present invention described in the claims. [References] [1] Poore, Joseph, and Thomas Nemecek. "Reducing food's environmental impacts through producers and consumers." Science 360.6392 (2018): 987-992. [2] Tilman, David, et al. "Agricultural sustainability and intensive production practices." Nature 418.6898 (2002): 671-677. [3]IPCC, 2018: Summary for Policymakers. In: Global Warming of 1.5℃. https: / / www.ipcc.ch / site / assets / uploads / sites / 2 / 2019 / 05 / SR15_SPM_version_report_LR.pdf. [4]Grafton, R. Quentin, John Williams, and Qiang Jiang. "Food and water gaps to 2050: preliminary results from the global food and water system (GFWS) platform." Food Security 7.2 (2015): 209-220. [5] Myers, Samuel S., et al. "Climate change and global food systems: potential impacts on food security and undernutrition." Annual review of public health 38.1 (2017): 259-277. [6] Rickson, R. J., et al. "Input constraints to food production: the impact of soil degradation." Food security 7.2 (2015): 351-364. [7]P Hawken et al. (2017). "Project Drawdown: Table of Solutions." https: / / www.drawdown.org / solutions / table-of-solutions [8] Rizvi, Sarah, et al. "Global land use implications of dietary trends." PloS one 13.8 (2018): e0200781. [9] Sandstrom, Vilma, et al. "The role of trade in the greenhouse gas footprints of EU diets." Global food security 19 (2018): 48-55.

[10] Food and Agriculture Organization of the United Nations. FAOSTAT Statistical Database. [Rome] :FAO, 1997.

[11] Cuellar, Amanda D., and Michael E. Webber. "Wasted food, wasted energy: the embedded energy in food waste in the United States." Environmental science & technology 44.16 (2010): 6464-6469.

[12] Birney, Catherine I., et al. "An assessment of individual foodprints attributed to diets and food waste in the United States." Environmental Research Letters 12.10 (2017): 105008.

[13] Tonini, Davide, Paola Federica Albizzati, and Thomas Fruergaard Astrup. "Environmental impacts of food waste: Learnings and challenges from a case study on UK." Waste Management 76 (2018): 744-766.

[14] Scherhaufer, Silvia, et al. "Environmental impacts of food waste in Europe." Waste management 77 (2018): 98-113.

[15] Shopper Insight (2021). Real Shopper SM. http: / / shopperinsight.co.jp / service / .

[16] Ministry of Agriculture, Forestry and Fisheries (2019). "Food Price Trend Survey". https: / / www.maff.go.jp / j / zyukyu / anpo / kouri / k_yasai / h22index.html [Explanation of symbols]

[0068] 100 Environmental Load Estimation Device 110 POS-DB (database) 120 Food Price DB 130 Meta-analysis DB 140 Data Acquisition Section 150 Food Consumption Weight Estimation Division 160 Environmental Impact Estimation Division 1000 Drive Device 1001 Recording media 1002 Auxiliary storage 1003 Memory device 1004 CPU 1005 Interface device 1006 Display device 1007 Input Device

Claims

1. An environmental impact estimation device for estimating the environmental impact of food, comprising: a food consumption weight estimation unit that estimates a consumption weight of the food by dividing daily sales amount data of the food obtained from the POS data by a value obtained by multiplying price data per unit weight of the food by a market ratio of the POS data; an environmental load estimation unit that estimates an environmental load of the food by multiplying the consumption weight estimated by the food consumption weight estimation unit by an environmental load coefficient of the food; An environmental impact estimation device equipped with:

2. The environmental load estimation unit obtains the environmental load coefficient from a meta-analysis database that stores the environmental load coefficient obtained by a meta-analysis of each food. The environmental load estimation device according to claim 1 .

3. The environmental load estimation unit calculates a total amount of environmental load for a plurality of foods by adding up the environmental loads for the plurality of foods. The environmental load estimation device according to claim 1 or 2.

4. An environmental load estimation method implemented by an environmental load estimation device that estimates environmental loads of food, comprising: a food consumption weight estimation step of estimating a consumption weight of the food by dividing daily sales amount data of the food obtained from POS data by a value obtained by multiplying price data per unit weight of the food by a market share of the POS data; an environmental load estimation step of estimating an environmental load of the food by multiplying the consumption weight estimated in the food consumption weight estimation step by an environmental load coefficient of the food; A method for estimating environmental impacts.

5. A program for causing a computer to function as each unit in the environmental load estimation device according to any one of claims 1 to 3.

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