Simulation evaluation method for food consumption cross-regional environmental influence

Abstract

The invention discloses a food consumption cross-regional environmental influence simulation evaluation method, which comprises the following steps of: selecting a reference input-output table, and collecting food consumption data and environmental satellite account data which are consistent with or similar to the reference input-output table in year; processing the food consumption data to generate a food consumption final demand vector; processing the environmental satellite account data to generate a second environmental satellite account consistent with the reference input-output table in structure; constructing an environment footprint evaluation model of food consumption; according to the method, the multi-region input-output model is applied to the environmental economics, so that the cross-region environmental influence generated by the food consumption can be vividly described; compared with current mainstream input-output database and model research, the method has higher department resolution, and can be combined with an environmental satellite account to meet multi-dimensional requirements in environmental influence research.

Description

Technical Field

[0001] The present invention belongs to the field of environmental economic research, involving industrial ecology, urban ecology and environmental economics, and in particular to a simulation and assessment method for the cross-regional environmental impact of food consumption, which is suitable for research on environmental science, urban sustainable development and the impact of food consumption. Background Art

[0002] As an economic analysis tool, the Multi-Region Input-Output Model (MRIO) has been widely used in the study of cross-regional industrial linkages. By establishing inter-regional industry input-output tables, it can depict the flow of products and services between regions. The recently developed environmentally extended MRIO model, combined with environmental satellite accounts, has been widely used to study the source tracing and pollution transfer of environmental emissions from industrial activities such as electricity and energy.

[0003] However, the current mainstream global multi-regional input-output databases have low resolution for the agricultural sector, typically covering only one or two agricultural sectors. They also lack a detailed food product sector, making it difficult to model cross-regional food flows. The few databases that do include a detailed food sector (such as EXIO base) also have relatively low regional resolution. Meanwhile, databases that offer both a detailed food sector and high regional resolution (such as GTAP) lack environmental satellite account data. Consequently, existing global input-output databases struggle to simultaneously meet the requirements of high food category resolution, high regional resolution, and multi-dimensional environmental satellite accounts.

[0004] Therefore, it is necessary to integrate a variety of global input-output databases, build an input-output model that meets the cross-regional flow of food's environmental footprint, trace the food's environmental footprint at the consumption site, and provide data support for defining the responsibility for agricultural environmental emissions at the consumption and production sites. Summary of the Invention

[0005] The purpose of the present invention is to provide a simulation assessment method for the cross-regional environmental impact of food consumption, which can meet the needs of analyzing the cross-regional flow of food's environmental footprint and provide data support for the definition of responsibility for agricultural environmental emissions in consumption and production areas.

[0006] In order to achieve the above object, the solution of the present invention is:

[0007] A simulation and assessment method for the cross-regional environmental impact of food consumption comprises the following steps:

[0008] Step 1: Select a benchmark input-output table and collect food consumption data and environmental satellite account data for the same or similar year as the benchmark input-output table;

[0009] Step 2: Process the food consumption data to generate a final food consumption demand vector;

[0010] Step 3: Process the environmental satellite account data to generate a second environmental satellite account having a structure consistent with the benchmark input-output table;

[0011] Step 4: Construct an environmental footprint assessment model for food consumption;

[0012] Step 5: Decompose the environmental footprint by region and analyze the cross-regional flow pattern of the environmental footprint of food consumption.

[0013] Preferably, step 1 is achieved by: selecting an input-output database as a benchmark input-output table based on the scope of the food trade network in the case area and the high-resolution requirements for the food sector; and collecting food consumption data and environmental emission data in the same or similar years based on the time scale of the benchmark input-output table.

[0014] Preferably, in step 2, the processing of the food consumption data includes food classification and integration, matching the final demand of the input-output table and allocation of food sources.

[0015] Preferably, the physical quantity of food consumption is converted into the final demand for food consumption using a food buyer's price calculation formula, which is:

[0016]

[0017] Among them, P i s represents the purchaser price of food sector i in country s where the case city is located; represents the final consumer demand of country s for food sector i from region r, represents the final household consumption demand of food sector i in country s for all supply locations, which is obtained from the benchmark input-output table; represents the total food supply for household consumption in food sector i of country s, which is obtained from the food balance sheets of the FAO database.

[0018] Preferably, based on the trade structure in the benchmark input-output table, the supply data of different food sectors in the case city are obtained, and the food consumption of the case city is allocated to each supply area according to the supply ratio of different regions, thereby generating the final food consumption demand vector of the case area:

[0019]

[0020] in, represents the proportion of food sector i from supply location r to food sector i in country s where the case city is located, represents the final consumer demand of country s for food sector i from region r, represents the final resident consumption demand of food sector i in country s for all supply locations, which is obtained from the benchmark input-output table.

[0021] Preferably, in step 3, the processing of the environmental satellite account data includes: industry matching and region matching, wherein the industry matching is to match the departments of the environmental satellite account to the departments of the benchmark input-output table according to the international industry standard matching code.

[0022] Preferably, the regional matching is: for regions where the country name in the environmental satellite account is consistent with the country name in the benchmark input-output table, the environmental satellite account remains unchanged; for regions where countries are aggregated, the countries that are not matched in the benchmark input-output table are mapped one by one according to the definition of the aggregation area, and the differences in technological levels of various countries and departments in the aggregation area are taken into account during the mapping process to carry out differentiated allocation of emission intensity.

[0023] Preferably, the differentiated allocation of emission intensity is achieved through the following steps:

[0024] Step 321, identifying aggregation areas with high emission contributions and their leading industries;

[0025] Step 322, performing regional differentiated allocation of emission intensity of the leading industries in the aggregation area;

[0026] Step 323: Perform sector-differentiated allocation of emission intensity for the leading industrial sectors in the same country.

[0027] Preferably, the construction content of the food consumption environmental footprint assessment model in step 4 is: the final food consumption demand vector generated in step 2 is defined as y, the environmental emission intensity vector in the second environmental satellite account processed in step 3 is defined as E, and the two are placed in the corresponding data column of the benchmark input-output table to construct the food system environmental footprint assessment model:

[0028]

[0029] Where F represents the total environmental emissions, i.e., the environmental footprint; E represents the corresponding environmental emission intensity in the second environmental satellite account; X represents the total output; (IA) -1 is the Leontief inverse matrix; A is composed of a ij The direct consumption coefficient matrix, a ij represents the direct demand for the products or services of the designated industry j in the designated region per unit of product produced by the designated industry i in the designated region; Z is the product of z ij The intermediate flow matrix, z ijrepresents the direct demand of the specified industry i in the specified region for the products or services of the specified industry j in the specified region; y is the final demand column vector.

[0030] Preferably, the method for analyzing the cross-regional flow pattern of the environmental footprint of food consumption in step 5 is: F is composed of f i The column vector, f i The total environmental emissions from food consumption in a given industry within the case study region are represented by the total environmental emissions from food consumption in that region. Summing the emissions from all industries within the given region yields the total environmental emissions from food consumption in that region. Summing the emissions from the remaining regions reveals the distribution of environmental emissions driven by food consumption across the case study region, i.e., the regional flow pattern of the environmental footprint driven by food consumption in the case study region.

[0031] After adopting the above scheme, compared with the existing technology, the present invention applies the multi-regional input-output model to environmental economics, which can vividly depict the cross-regional environmental impact of food consumption. It has higher sector resolution than the current mainstream input-output database and model research, and can be combined with environmental satellite accounts to meet the multi-dimensional needs in environmental impact research. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the present invention;

[0033] Figure 2 It is an embodiment to study selected food consumption data and environmental satellite account data;

[0034] Figure 3 is a schematic diagram of the process of processing food consumption data in the embodiment study;

[0035] Figure 4 It is a schematic diagram of the process of converting the input-output table of the environmental satellite account into the benchmark input-output table in the embodiment study;

[0036] Figure 5 It is a schematic diagram of the environmental footprint assessment model constructed in the embodiment study. DETAILED DESCRIPTION

[0037] The technical solutions and beneficial effects of the present invention will be described in detail below with reference to the accompanying drawings and examples. The example used is a simulation assessment of the cross-regional environmental impact of food consumption in the Guangdong-Hong Kong-Macao Greater Bay Area.

[0038] The present invention provides a simulation and assessment method for the cross-regional environmental impact of food consumption, which is based on a multi-region input-output model and can realize regional flow analysis of food environmental footprint based on a multi-region input-output database, such as Figure 1 As shown, the specific steps include:

[0039] S1: Select a benchmark input-output table and collect food consumption data and environmental satellite account data for the same or similar year as the benchmark input-output table.

[0040] 1.1 Based on the scope of the food trade network in the case region and the high-resolution requirements for the food sector, an input-output database is selected as the benchmark input-output table;

[0041] 1.2 Based on the time scale of the benchmark input-output table, collect food consumption data and environmental emission data for the same or similar years.

[0042] In the study of the embodiment, the 2014 input-output table in the GTAP 10 input-output database was selected as the benchmark based on the scope of the food trade network in the Guangdong-Hong Kong-Macao Greater Bay Area and the high-resolution requirements for the food sector. However, since the time scale of the benchmark input-output database selected in the embodiment is discontinuous, the data year selection was based on the year that was consistent with the benchmark input-output table, or the year difference was no more than one year.

[0043] like Figure 2 As shown, food consumption data and environmental emission inventory data are obtained, and the selected data need to be verified for authenticity and rationality. The embodiment uses the "Guangdong Statistical Yearbook" and the "Second Hong Kong Population Food Consumption Survey Report" as data sources, and collects data on the consumption of twelve kinds of food, including rice, wheat, other grains, fruits and vegetables, oilseeds, beef, pork, poultry, other meat products, eggs, milk, and aquatic products in nine cities in the Pearl River Delta and Hong Kong in 2015. The embodiment uses the CH4, CO2, and N2O emissions in the environmental satellite account of the EXIObase3 input-output database as the carbon emission inventory, NO X , NH3, and N emissions are used as nitrogen emission inventory, and P emissions are used as phosphorus emission inventory.

[0044] S2: Process the food consumption data to generate the final food consumption demand vector.

[0045] like Figure 3 As shown, step S2 includes food classification and integration, matching the final demand of the input-output table and the allocation of food sources.

[0046] 2.1 Food classification and integration: The original food consumption data are integrated according to the food sector types in the benchmark input-output table to facilitate subsequent matching with the input-output table.

[0047] 2.2 Matching the final demand of the input-output table: Since the data unit of the input-output table is in monetary form, it is necessary to convert the physical quantity of food consumption into the final demand for food consumption according to the food purchaser's price of the country where the case area is located. The research object of this example is the Guangdong-Hong Kong-Macao Greater Bay Area, so it is necessary to convert the physical quantity of food consumption into the final demand for food consumption of residents based on the food purchaser's price of China in that year. Among them, the formula for calculating the food purchaser's price of a certain country is as follows:

[0048]

[0049] Among them, P i s represents the purchaser price of food sector i in country s where the case city is located; represents the final consumer demand of country s for food sector i from region r, represents the final household consumption demand of food sector i in country s for all supply locations, which can be obtained from the benchmark input-output table; represents the total food supply for household consumption in food sector i of country s, which can be obtained from the food balance sheet of the FAO database.

[0050] 2.3 Food source allocation: Based on the trade structure in the benchmark input-output table, we obtain the supply data of different food sectors in the case city and allocate the food consumption of the case city to each supply area according to the supply ratio of different regions. Among them, the proportion of food sector i from supply area r to food sector i in region s of the case city is The calculation formula is as follows:

[0051]

[0052] in, represents the proportion of food sector i from supply location r to food sector i in country s where the case city is located, represents the final consumer demand of country s for food sector i from region r, represents the final consumer demand of food sector i in country s for all supply locations. This data is obtained from the benchmark input-output table. This generates a final food consumption demand vector for the case location. In this embodiment, a final food consumption demand vector for residents of the Guangdong-Hong Kong-Macao Greater Bay Area urban cluster can be generated.

[0053] S3: Process the environmental satellite account data to generate a second environmental satellite account with the same structure as the benchmark input-output table;

[0054] like Figure 4As shown, it shows the difference in regional resolution and departmental resolution between the input-output table of the environmental satellite account selected in the embodiment and the benchmark input-output table. Therefore, it is necessary to preprocess the environmental satellite account according to the data structure of 141 regions and 58 departments in the benchmark input-output table.

[0055] The environmental satellite account processing provided by the present invention includes the following two contents:

[0056] 3.1 Industry Matching: The sectors of the environmental satellite accounts are matched to those of the benchmark input-output table using the International Industry Standard Matching Code (ISIC). This step requires that the sector resolution of the benchmark input-output table be lower than that of the input-output table in which the environmental satellite accounts reside. In this example, the environmental satellite accounts for 200 sectors in 49 regions of EXIO base3 are merged using the sector structure of the benchmark input-output table using the International Industry Standard Matching Code (ISIC Rev. 4), generating a second environmental satellite account for 49 regions and 65 sectors.

[0057] 3.2 Regional Matching: For regions where country names in the environmental satellite accounts match those in the benchmark input-output table, the environmental satellite accounts remain unchanged. For regions where countries are aggregated, countries not matched in the benchmark input-output table are mapped one-to-one according to the definition of the aggregation region. The mapping process must account for differences in technological levels among countries and sectors within the aggregation region to allocate environmental emission intensities. In this example, the 97 countries in the benchmark input-output table are mapped one-to-one to the five aggregation regions of the environmental satellite accounts.

[0058] In combination with the embodiments, the present invention realizes the differentiated distribution of emission intensity of various countries and sectors in the aggregation area through three steps: identifying the aggregation area with high emission contribution and its dominant industries, making regional differentiated distribution of emission intensity of the dominant industries in the aggregation area, and making sector-differentiated distribution of emission intensity of the dominant industrial sectors of the same country.

[0059] 3.2.1 Identify clusters with high emission contributions and their leading industries: Consolidate the sectors in the environmental satellite accounts of the clusters into primary, secondary, and tertiary industries, and calculate the ratio of environmental emissions from all industries in each cluster to the total emissions of all countries. In this example, the environmental emissions of 65 sectors in five clusters in EXIO base3 were consolidated into the environmental emissions of the primary, secondary, and tertiary industries of the five clusters, and the ratio of total environmental emissions from each cluster to the total emissions of all countries was calculated.

[0060] For aggregation areas with an % concentration of less than 10%, there is no need to consider differences in technological levels. The emission intensity of the environmental satellite accounts of each country and department in the aggregation area is consistent with the emission intensity of the environmental satellite accounts of the corresponding departments in the aggregation area.

[0061] For clusters with a concentration greater than 10%, the dominant industries responsible for environmental emissions within the cluster are identified. Technological differences in the dominant industries need to be considered; technological differences in the non-dominant industries do not. That is, the emission intensities of the non-dominant industries in each country within the cluster are consistent with those of the non-dominant industries within the cluster. In this embodiment, NH3 emissions in cluster 5 are primarily influenced by the primary industry, so technological differences in NH3 emissions from the primary industry across all countries within the cluster need to be considered.

[0062] 3.2.2 Make regionally differentiated allocations of emission intensity of the leading industries in the agglomeration area:

[0063] Introducing environmental satellite accounts from other input-output databases requires that the accounts be of the same type and at the same regional scale as the existing ones, with higher regional resolution. This example introduces the environmental satellite accounts corresponding to the EORA input-output database. This database's environmental satellite accounts meet the requirements of this example, have a global regional scale, and a data structure covering 190 countries and 26 industries, with a higher regional resolution than the existing environmental satellite accounts.

[0064] The imported environmental satellite account sectors were merged into primary, secondary, and tertiary industries. The ratio of the environmental emissions of each country's leading industry within the aggregation area to the environmental emissions of the leading industries in the entire aggregation area was calculated, and this ratio was set as the technological proportion of the leading industries in each country within the aggregation area. In this example, the 26 industries in the EORA data were merged into primary, secondary, and tertiary industries. Using the NH3 environmental satellite account as an example, the ratio of NH3 emissions from the primary industry in the EORA database to the NH3 emissions from the primary industry in aggregation area 5 was calculated for each country within aggregation area 5. This ratio was set as the technological proportion of the primary industry in each country within aggregation area 5.

[0065] The existing environmental emissions of the dominant industries in the aggregation area are distributed to each country within the aggregation area according to the technology ratio, thereby obtaining the regional heterogeneous environmental emission intensity of the dominant industries in each country within the aggregation area. In this example, the NH3 emissions of the primary industry in aggregation area 5 are distributed to each country according to the technology ratio of EORA, thereby obtaining the heterogeneous NH3 environmental emission intensity of the primary industry in each country.

[0066] 3.2.3 Make differentiated allocations of emission intensity for the leading industrial sectors in the same country:

[0067] Match the imported leading industrial sectors of the environmental satellite accounts to the existing leading industrial sectors of the environmental satellite accounts. There are two ways to match sectors. The first is industry matching in S3. Since industry matching has a primary condition, if it is not met, refer to the second method. The second is product matching, matching the products produced by the leading industrial sector to the leading industrial sector. In this example, the primary industrial sector of a country in aggregation zone 5 of the EORA data is matched to the primary industrial sector of a country in aggregation zone 5 of the benchmark input-output table. There are two ways to match sectors. The first is industry matching. Since industry matching has a primary condition, if it is not met, refer to the second method. The second is product matching. In this example, the product sector produced by the primary industrial sector in the EORA data is matched to the primary industrial sector of the benchmark input-output table.

[0068] After matching, the ratio of environmental emissions of each sector in a country's leading industry to the total environmental emissions of the country's leading industry is calculated, and this ratio is set as the technology ratio of each sector within the country's leading industry. After sector matching, the embodiment calculates the ratio of NH3 emissions of each sector in a country's primary industry in the EORA database to the total NH3 emissions of the country's primary industry, and sets this ratio as the technology ratio of each sector within the country's primary industry.

[0069] The original environmental emissions of the country's leading industry are allocated to each department within the country's leading industry according to the technical ratio, thereby obtaining the sector-specific heterogeneous environmental emission intensity of each department within the country's leading industry. In this embodiment, the NH3 emissions of the country's primary industry are allocated to each department within the country's primary industry according to the technical ratio of EORA, thereby obtaining the heterogeneous NH3 environmental emission intensity of each department within the country's primary industry.

[0070] The above steps are repeated for all aggregation areas to generate the second environmental satellite accounts with the same structure as the benchmark input-output table.

[0071] S4: Construct an environmental footprint assessment model for food consumption;

[0072] The model building content of this step is:

[0073] The final food consumption demand vector generated by S2 is defined as y, and the environmental emission intensity vector in the second environmental satellite account after S3 processing is defined as E, which is placed in the corresponding data column of the benchmark input-output table. In this way, the environmental footprint assessment model of the food system is constructed, as shown in the following example: Figure 5 shown.

[0074] The calculation formula of the environmental footprint model is as follows:

[0075]

[0076] Where F represents the total environmental emissions, i.e., the environmental footprint; E represents the corresponding environmental emission intensity in the second environmental satellite account; X represents the total output; (IA) -1 is the Leontief inverse matrix; A is composed of a ij The direct consumption coefficient matrix, a ij It represents the direct demand for products or services of industry j in a certain region per unit of product produced by industry i in a certain region; Z is the product of z ij The intermediate flow matrix, z ij represents the direct demand of a certain industry i in a certain region for the products or services of an industry j in another region; y is the final demand column vector.

[0077] S5: Decompose the environmental footprint by region and analyze the cross-regional flow pattern of the environmental footprint of food consumption. The specific evaluation and analysis method is as follows: F is composed of f i The column vector, f i The total environmental emissions from food consumption in the case study region are represented by the environmental emissions of industry i in a given region. Summing the environmental emissions from all industries in the region yields the total environmental emissions from food consumption in the case study region. Summing the emissions from the remaining regions reveals the distribution of environmental emissions driven by food consumption in the case study region, i.e., the regional flow pattern of the environmental footprint driven by food consumption in the case study region.

[0078] The regional flow pattern of environmental footprints can explain how much environmental emissions embodied in food are transferred to the case by consumers in the case area through trade networks, thereby leading to the problem of unfair distribution of environmental responsibilities.

[0079] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A simulation and assessment method for the cross-regional environmental impact of food consumption, characterized by The following steps are involved: Step 1: Select a benchmark input-output table and collect food consumption data and environmental satellite account data for the same or similar year as the benchmark input-output table; Step 2: Process the food consumption data to generate a final food consumption demand vector; Step 3: Process the environmental satellite account data to generate a second environmental satellite account having a structure consistent with the benchmark input-output table; Step 4: Construct an environmental footprint assessment model for food consumption; Step 5: Decompose the environmental footprint by region and analyze the cross-regional flow pattern of the environmental footprint of food consumption.

2. A simulation and assessment method for the cross-regional environmental impact of food consumption according to claim 1, characterized in that Step 1 is achieved by: selecting an input-output database as a benchmark input-output table based on the scope of the food trade network in the case area and the high-resolution requirements for the food sector; and collecting food consumption data and environmental emission data in the same or similar years based on the time scale of the benchmark input-output table.

3. The method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 1, characterized in that In step 2, the processing of the food consumption data includes food classification and integration, matching the final demand of the input-output table and the allocation of food sources.

4. A simulation and assessment method for the cross-regional environmental impact of food consumption according to claim 3, characterized in that The physical quantity of food consumption is converted into the final demand for food consumption using the food buyer's price calculation formula. The food buyer's price calculation formula is: in, represents the purchaser price of food sector i in country s where the case city is located; represents the final consumer demand of country s for food sector i from region r, represents the final household consumption demand of food sector i in country s for all supply locations, which is obtained from the benchmark input-output table; represents the total food supply for household consumption in food sector i of country s, which is obtained from the food balance sheets of the FAO database.

5. The method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 3, characterized in that According to the trade structure in the benchmark input-output table, we obtain the supply data of different food sectors in the case city, and allocate the food consumption of the case city to each supply area according to the supply ratio of different regions. In this way, we generate the final demand vector of food consumption in the case area: in, represents the proportion of food sector i from supply location r to food sector i in country s where the case city is located, represents the final consumer demand of country s for food sector i from region r, represents the final resident consumption demand of food sector i in country s for all supply locations, which is obtained from the benchmark input-output table.

6. The method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 1, characterized in that In step 3, the processing of the environmental satellite account data includes: industry matching and region matching. The industry matching is to match the departments of the environmental satellite account to the departments of the benchmark input-output table according to the international industry standard matching code.

7. A method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 6, characterized in that The regional matching is as follows: for regions where the country names in the environmental satellite accounts are consistent with those in the benchmark input-output table, the environmental satellite accounts remain unchanged; for regions where countries are aggregated, the countries that are not matched in the benchmark input-output table are mapped one by one according to the definition of the aggregation region. During the mapping process, the differences in technological levels of various countries and departments in the aggregation region are taken into account to carry out differentiated allocation of emission intensity.

8. The method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 7, characterized in that The differentiated allocation of emission intensity is achieved through the following steps: Step 321, identifying aggregation areas with high emission contributions and their leading industries; Step 322, performing regional differentiated allocation of emission intensity of the leading industries in the aggregation area; Step 323: Perform sector-differentiated allocation of emission intensity for the leading industrial sectors in the same country.

9. The method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 1, characterized in that The construction content of the food consumption environmental footprint assessment model in step 4 is as follows: the final food consumption demand vector generated in step 2 is defined as y, the environmental emission intensity vector in the second environmental satellite account processed in step 3 is defined as E, and the two are placed in the corresponding data column of the benchmark input-output table to construct the food system environmental footprint assessment model: Where F represents the total environmental emissions, i.e., the environmental footprint; E represents the corresponding environmental emission intensity in the second environmental satellite account; X represents the total output; (IA) -1 is the Leontief inverse matrix; A is composed of a ij The direct consumption coefficient matrix, a ij represents the direct demand for the products or services of the designated industry j in the designated region per unit of product produced by the designated industry i in the designated region; Z is the product of z ij The intermediate flow matrix, z ij represents the direct demand of the specified industry i in the specified region for the products or services of the specified industry j in the specified region; y is the final demand column vector.

10. The method for simulating and evaluating the cross-regional environmental impact of food consumption according to claim 1, characterized in that The analysis method for the cross-regional flow pattern of the environmental footprint of food consumption in step 5 is: F is composed of f i The column vector, f i The total environmental emissions from food consumption in a given industry within the case study region are represented by the total environmental emissions from food consumption in that region. Summing the emissions from all industries within the given region yields the total environmental emissions from food consumption in that region. Summing the emissions from the remaining regions reveals the distribution of environmental emissions driven by food consumption across the case study region, i.e., the regional flow pattern of the environmental footprint driven by food consumption in the case study region.

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