Environmental footprint prediction method based on input-output model
Through the method based on input-output model, combining historical data and forecast data, dynamic simulation technology progress and population changes, the accuracy and applicability of environmental footprint prediction are solved, and a more scientific future environmental footprint prediction is achieved.
Patent Information
- Application Number
- CN202510374922.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-27
- Publication Date
- 2025-07-22
AI Technical Summary
When predicting environmental footprints, existing research is difficult to dynamically simulate the complex effects of technological progress, economic growth and population changes, and lacks a systematic and standardized analytical framework, resulting in insufficient accuracy and applicability of the prediction results.
Using an input-output model-based method, we use historical data, double logarithmic regression processing, and combined with population and economic forecast data to build an environmentally expanded multi-regional input-output model to calculate future environmental footprints.
It has achieved dynamic coupled feedback on the linkage effect of technological progress and consumption structure changes, improved the scientificity and practical guiding significance of environmental footprint prediction, and overcomes the lack of adaptability of traditional methods.
Smart Images
Figure CN120355255A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of environmental footprint assessment, and particularly relates to a prediction method for environmental footprint based on an input-output model. Background Art
[0002] Under the background of the continuous intensification of global economic integration and ecological environment pressure, the Environmentally Extended Multi-Regional Input-Output Model (EEMRIO) has become an important tool for analyzing the coupling relationship between the ecological environment and the economic system because it can comprehensively consider the inter-industry relevance, the regional resource mobility, and the whole-chain effect of environmental impacts. Compared with the traditional single-regional input-output model, EEMRIO has the advantages of comprehensiveness, systematicness, and accuracy, and can evaluate the environmental impacts of economic activities from the dual perspectives of the production side and the consumption side.
[0003] Technological progress, economic growth, and population expansion are the main factors driving ecological environment changes. However, existing studies often view these factors in isolation when analyzing them, lacking an organic combination with the economic input-output relationship, and it is difficult to dynamically simulate the complex impacts of multiple factors on the environmental footprint. In addition, the current research technology for predicting environmental footprints is still in its infancy, especially regarding how to simultaneously incorporate the dynamic impacts of technological progress, economic growth, and population changes into the EEMRIO model, and a systematic and standardized analysis framework has not yet been formed. Therefore, there is an urgent need in the market for a dynamic environmental footprint prediction technology that can combine technological progress, economic growth, and population expansion to make up for the deficiencies in accuracy, applicability, and dynamics of existing studies. Summary of the Invention
[0004] One aspect of the present invention provides a prediction method for environmental footprint based on an input-output model, including the following steps: Collect multi-regional input-output tables, income elasticity data, and regional environmental indicator intensities for historical years; Based on the multi-regional input-output tables for historical years and the regional environmental indicator intensities, determine the multi-regional input-output matrix and the environmental indicator intensity per unit output; Combined with the prediction period, perform double logarithmic regression on the historical data of the environmental indicator intensity per unit output in each region, assign non-numeric and negative values as 0 values, and obtain the predicted data of the environmental indicator intensity per unit output in each region; Calculate the predicted data of the total population and the total economic volume; combined with the income elasticity data, the predicted data of the total population, and the predicted data of the total economic volume, couple the final demand items of the multi-regional input-output table, and calculate the change amount of the final demand in each region brought about by population changes and consumption structure changes; Based on the predicted data of the final demand change amounts in each region and the environmental intensity indicators per unit output in each region, construct an environmentally extended multi-regional input-output model to calculate the environmental footprint of each region in the future under the condition that the production structure remains unchanged.
[0005] As a preferred technical solution, the income elasticity data includes the income elasticity coefficients of the regions and sectors corresponding to the multi-regional input-output table; The historical data of the environmental indicator intensities in each region includes carbon emissions, land use, and water resources.
[0006] As a preferred technical solution, the input-output matrix includes an intermediate input matrix, a final demand matrix, a value-added vector, and a total output vector, specifically: Assume there are R regions, and each region has N sectors. The dimension of the intermediate input matrix Z is (R*N)*(R*N), expressed as: ; where each element represents the intermediate input provided by sector i in region r to sector j in region s; The dimension of the final demand matrix Y is (R*N)*(R*M), where M represents the types of final demands. Combine all types of final demands into the total final demand quantity to obtain the final demand matrix Y, expressed as: ; The value-added vector is expressed as , and the total output vector is expressed as ; The direct consumption coefficient matrix represents the input quantity of products from other sectors required for the production of a unit product, which is obtained by dividing the intermediate input matrix by the total output vector and is defined as , and the matrix form is expressed as: .
[0007] As a preferred technical solution, the environmental indicator intensity per unit output is obtained by the following steps: Invert the direct consumption coefficient matrix to obtain the complete consumption coefficient matrix, that is B = ( I - A ) -1 ; I is an identity matrix with all diagonal elements being 1 and non-diagonal matrices being 0, A is the direct consumption coefficient matrix, B is the complete consumption coefficient matrix; Extract the environmental data by source from the environmental indicator database with a spatial resolution of 1°×1° and an annual time resolution to obtain grid-based, annualized, and sectoral global environmental data, and then match it to the satellite account vector of the global multi-regional input-output table; Divide the satellite account vector by the total output vector to obtain the environmental coefficient per unit output, that is, the environmental indicator intensity per unit output.
[0008] As a preferred technical solution, perform double-logarithmic regression on the historical data of the environmental indicator intensity per unit output in each region and obtain the predicted data of the environmental indicator intensity per unit output in each region. Specifically: Take the logarithm of the environmental indicator intensity per unit output for multiple historical years respectively, and assign the infinite value to 0; Based on the logarithm of the determined environmental indicator intensity per unit output and the historical years, fit out s Region i The linear regression model of the average annual growth rate of the environmental indicator intensity per unit output of the sector with respect to the year is as follows: ; Among them, Represents s Region i The environmental intensity of the sector, year Represents the historical year, Represents the error, Represents the base year s Region i The logarithm of the environmental intensity of the sector, Represents that for each additional year, s Region i The average change in the logarithm of the environmental intensity of the sector; if , it indicates that the environmental intensity shows an upward trend; if , it indicates that s Region i The environmental intensity of the sector shows a downward trend with the increase of the year; Substitute the predicted year into the linear regression model to obtain the value of the environmental indicator intensity per unit output of the corresponding country or region in a future predicted year.
[0009] As a preferred technical solution, calculate the predicted data of the total population and the predicted data of the total economic volume. Specifically: Based on the historical total population data, current trends, and influencing factors, estimate the population quantity results of each region in a future period through a statistical model to obtain the predicted data of the total population; Based on historical economic aggregate data, current economic status, and influencing factors, economic variables in each region are predicted for a future time period through statistical models, econometric methods, or artificial intelligence methods to obtain quantitative results and derive economic aggregate prediction data.
[0010] As an optimal technical solution, the final demand items of the coupled multi-regional input-output table are used to calculate the change in final demand in each region brought about by population changes and consumption structure changes, specifically as follows: Based on the historical population total data and historical economic aggregate data of the base year, the population growth rate and economic growth rate in the predicted year under different scenarios are obtained; Using the final demand item and the population growth rate in the predicted year under different scenarios, the change in final demand brought about by regional population changes is obtained, which is expressed by the formula: s ; ; where, represents t the new final demand quantity generated after the population change in the s region, represents the final demand quantity in the base period s of the region, s represents the population growth rate of the region; According to the multi-regional input-output table, the target region and sector are determined, the income elasticity data of the target region and sector are obtained, and the income elasticity coefficient is logarithmically processed; Based on the change ratio of GDP in the future year and the income elasticity data, considering the t period s final demand quantity of the region is expressed as: where, represents t the new final demand quantity generated after economic growth and population change in the s region, represents s the GDP growth rate of the region, s represents i the income elasticity coefficient of the region's s sector; Considering the impact of income growth, the sector consumption structure of the s region represents s the proportion of the demand of each sector in the region's total demand, which is expressed as: Combined with s the change in the sector consumption structure of the t periods Final demand of the region Expressed as: ; Aggregated to the regional scale, s The change in the final demand structure of each sector caused by regional economic growth and population change generates a new final demand volume, expressed as: .
[0011] As a preferred technical solution, the environmental extended multi-regional input-output model is constructed to calculate the environmental footprint of each region in the future under the condition that the production structure remains unchanged, specifically: Obtained from the perspective of the demand side t The environmental footprint of each region in the world during the [[[ID=]]] period is expressed as: .
[0012] Another aspect of the present invention also provides a prediction system for the environmental footprint based on the input-output model, which is applied to the above-mentioned prediction method for the environmental footprint based on the input-output model, and includes a data acquisition module, a unit output environmental index intensity prediction module, a final demand change amount calculation module, and an environmental footprint prediction module; The data acquisition module is used to collect the multi-regional input-output table, income elasticity data, and environmental index intensity of each region in historical years; The unit output environmental index intensity prediction module is used to determine the multi-regional input-output matrix and the unit output environmental index intensity based on the multi-regional input-output table and the environmental index intensity of each region in historical years; combined with the prediction period, the historical data of the unit output environmental index intensity of each region is subjected to double logarithmic regression, and non-numbers and negative values are assigned as 0 values to obtain the predicted data of the unit output environmental index intensity of each region; The final demand change amount calculation module is used to calculate the predicted data of the total population and the total economic volume; combined with the income elasticity data, the predicted data of the total population and the total economic volume, the final demand items of the multi-regional input-output table are coupled to calculate the change amount of the final demand of each region brought about by population change and consumption structure change; The environmental footprint prediction module is used to construct an environmental extended multi-regional input-output model based on the change amount of the final demand of each region and the predicted data of the environmental intensity index per unit output of each region, and calculate the environmental footprint of each region in the future under the condition that the production structure remains unchanged.
[0013] Another aspect of the present invention also provides a storage medium storing a program, which when executed by a processor, implements the above-mentioned prediction method for the environmental footprint based on the input-output model.
[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) From the perspective of the consumption side, the present invention comprehensively considers the dynamic coupling feedback mechanism of technological progress, economic growth, and population expansion on the environmental footprint, which is significantly different from the static or single assumptions of these key driving factors in traditional research methods.
[0015] (2) The present invention innovatively incorporates the linkage effects of technological progress, changes in consumption structure, and changes in consumption level into the prediction framework, solving the problem in traditional methods that the long-term impacts of technological change and economic growth on the environmental footprint cannot be accurately captured.
[0016] (3) By optimizing the parameter settings and dynamic simulation capabilities of the input-output model, the present invention effectively overcomes the problem of insufficient adaptability of traditional prediction methods for future scenario simulation, making the prediction results more scientific and practically guiding. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 is a flowchart of a prediction method for environmental footprint based on an input-output model according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0018] In order to enable those skilled in the art of the present technology to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the scope of protection of the present application.
[0019] Embodiment: As Figure 1 shown, this embodiment provides a prediction method for environmental footprint based on an input-output model, including the following steps: S1: Acquisition of original data, where the original data includes multi-regional input-output tables for historical years, income elasticity data, socio-economic prediction data for each region, and environmental indicator intensities for each region.
[0020] In one or more preferred embodiments, the income elasticity data includes income elasticity coefficients for regions and sectors corresponding to the multi-regional input-output tables; the historical data of the environmental indicator intensities includes carbon emissions, land use, water resources, etc.; the socio-economic prediction data for each region includes total population prediction data and total economic volume prediction data for each region in the world.
[0021] S2: Based on the multi-regional input-output tables for historical years and the environmental indicator intensities for each region, determine the multi-regional input-output matrix and the environmental indicator intensity per unit output.
[0022] In one or more preferred embodiments, the input-output matrix includes an intermediate input matrix, a final demand matrix, a value-added vector, and a total output vector, specifically as follows: Assume there are R regions, and each region has N sectors. The dimension of the intermediate input matrix Z is (R*N)*(R*N), expressed as: ; where each element represents the intermediate input provided by sector i in region r to sector j in region s; The dimension of the final demand matrix Y is (R*N)*(R*M), where M represents the types of final demand. By combining all types of final demand into the total final demand quantity, the final demand matrix Y is obtained, expressed as:
[0023] The value-added vector is expressed as , and the total output vector is expressed as ; The direct consumption coefficient matrix represents the input quantity of products from other sectors required for the production of a unit product, which is obtained by dividing the intermediate input matrix by the total output vector and is defined as , and the matrix form is expressed as: .
[0024] In one or more preferred embodiments, the environmental indicator intensity per unit output is obtained by the following steps: Taking the inverse of the direct consumption coefficient matrix to obtain the complete consumption coefficient matrix, that is B = ( I - A ) -1 ; I is an identity matrix with diagonal elements all being 1 and non-diagonal matrices being 0, A is the direct consumption coefficient matrix, B is the complete consumption coefficient matrix; the complete consumption coefficient matrix reflects the relationship between the final demand vector and the total output vector, revealing the intricate industrial linkage relationships among regions; Extract the environmental data by source from the environmental indicator database with a spatial resolution of 1°×1° and a temporal resolution of one year to obtain gridded, annualized, and sectoral global environmental data, and then match it to the satellite account vector of the global multi-regional input-output table; the satellite account is usually presented in the form of a vector, representing the consumption of the flows of major natural resources, water pollutants, air pollutants, solid waste pollutants, etc. by each region and each sector; The unit output environmental coefficient is obtained by dividing the satellite account vector by the total output vector, that is, the unit output environmental index intensity.
[0025] S3: Combining the prediction period, the historical data of the unit output environmental index intensity in each region is subjected to double logarithmic regression, and non-numerical and negative values are assigned a value of 0 to obtain the predicted data of the unit output environmental index intensity in each region.
[0026] In one or more preferred embodiments, the double logarithmic regression of the historical data of the unit output environmental index intensity in each region and the obtaining of the predicted data of the unit output environmental index intensity in each region are specifically as follows: Logarithmize the unit output environmental index intensity for multiple historical years respectively, and assign a value of 0 to the infinite value among them; Based on the logarithm values of the determined unit output environmental index intensity and the historical years, fit out s Region i The linear regression model of the average annual growth rate of the unit output environmental index intensity of the department with respect to the year is as follows: ; Among them, Represents s Region i The environmental intensity of the department, year Represents the historical year, Represents the error, Represents the base year s Region i The logarithm value of the environmental intensity of the department, Represents that for each additional year of the year, s Region i The average change in the logarithm of the environmental intensity of the department; if , it indicates that the environmental intensity shows an upward trend; if , it indicates that s Region i The environmental intensity of the department shows a downward trend with the increase of the year; Substitute the predicted year into the linear regression model to obtain the value of the unit output environmental index intensity corresponding to a certain future predicted year in the country or region.
[0027] S4: Combining the income elasticity data, the total population prediction data, and the total economic volume prediction data, coupling the final demand items of the multi-regional input-output table, and calculating the change amount of the final demand in each region brought about by the population change and the consumption structure change.
[0028] In one or more preferred embodiments, the total population prediction data and the total economic volume prediction data are calculated according to the following steps: Based on historical total population data, current trends, and influencing factors, through statistical models, estimate the population quantity results in each region during a future period, and obtain the total population prediction data; Based on historical total economic volume data, current economic status, and influencing factors, predict economic variables in each region during a future time period through statistical models, econometric methods, or artificial intelligence methods, obtain quantitative results, and obtain the total economic volume prediction data.
[0029] In one or more preferred embodiments, couple the final demand items of the multi-regional input-output table, and calculate the change in final demand in each region brought about by population changes and consumption structure changes. Specifically: Based on the historical total population data and historical total economic volume data of the base year, calculate the population growth rate and economic growth rate in the predicted year under different scenarios; Using the final demand item and the population growth rate in the predicted year under different scenarios, obtain s The change in final demand quantity brought about by regional population changes, expressed by the formula: ; Wherein, represents t period s The new final demand quantity generated after the population change in the represents the base period s The final demand quantity in the represents s The population growth rate in the According to the multi-regional input-output table, determine the target region and sector, obtain the income elasticity data of the target region and sector, and logarithmize the income elasticity coefficient; Based on the change ratio of GDP in the future year and the income elasticity data, considering the t period s The final demand quantity in the ; Wherein, represents t period s The new final demand quantity generated after economic growth and population change in the represents s The GDP growth rate in the represents s region i The income elasticity coefficient of the Considering the impact of income growth, s The consumption structure of the s region represents the proportion of the demand of each department in the s region's total demand, expressed as: ; Combined with s the change in the consumption structure of departments in the region, t the s final demand of the region is expressed as: ; Aggregated to the regional scale, s the change in the final demand structure of each department caused by regional economic growth and population change generates a new final demand volume, which is expressed as: .
[0030] S5: Based on the final demand change amounts of each region obtained in step S4 and the predicted data of the environmental intensity index obtained in step S3, construct an environmentally extended multi-regional input-output model, and calculate the environmental footprint of each region in the future under the condition that the production structure remains unchanged.
[0031] In one or more preferred embodiments, the constructing an environmentally extended multi-regional input-output model and calculating the environmental footprint of each region in the future under the condition that the production structure remains unchanged is specifically: Considering technological progress, economic growth and population growth, obtain from the perspective of the demand side t the environmental footprint of each region in the .
[0032] In another embodiment of the present application, a prediction system for the environmental footprint based on an input-output model is provided. The system includes a data collection module, a unit output environmental index intensity prediction module, a final demand change amount calculation module, and an environmental footprint prediction module; The data collection module is used to collect the multi-regional input-output table, income elasticity data, and environmental index intensity of each region in historical years; The unit output environmental index intensity prediction module is used to determine the multi-regional input-output matrix and the unit output environmental index intensity based on the multi-regional input-output table and the environmental index intensity of each region in historical years; combined with the prediction period, perform double logarithmic regression on the historical data of the unit output environmental index intensity of each region, assign non-numeric and negative values to 0, and obtain the predicted data of the unit output environmental index intensity of each region; The final demand change amount calculation module is used to calculate the predicted data of the total population and the total economic volume; combined with the income elasticity data, the predicted data of the total population and the total economic volume, couple the final demand items of the multi-regional input-output table, and calculate the final demand change amounts of each region brought about by population change and consumption structure change; The environmental footprint prediction module is used to construct an environmentally extended multi-regional input-output model based on the predicted data of the final demand changes in each region and the environmental intensity indicators per unit output in each region, and calculate the environmental footprints of each region in the future under the condition that the production structure remains unchanged.
[0033] It should be noted here that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above. This system is an environmental footprint prediction method based on the input-output model applied to the above embodiment.
[0034] In another embodiment of the present application, a storage medium is further provided, storing a program, which, when executed by a processor, implements an environmental footprint prediction method based on the input-output model. Specifically: Collect the multi-regional input-output tables, income elasticity data, and environmental indicator intensities of each region in historical years; Based on the multi-regional input-output tables and environmental indicator intensities of each region in historical years, determine the multi-regional input-output matrix and the environmental indicator intensities per unit output; Combined with the prediction period, perform double logarithmic regression on the historical data of the environmental indicator intensities per unit output of each region, assign non-numeric and negative values to 0 values, and obtain the predicted data of the environmental indicator intensities per unit output of each region; Calculate the predicted data of the total population and the total economic volume; combined with the income elasticity data, the predicted data of the total population, and the predicted data of the total economic volume, couple the final demand items of the multi-regional input-output table, and calculate the final demand changes in each region brought about by population changes and consumption structure changes; Based on the predicted data of the final demand changes in each region and the predicted data of the environmental intensity indicators per unit output in each region, construct an environmentally extended multi-regional input-output model, and calculate the environmental footprints of each region in the future under the condition that the production structure remains unchanged.
[0035] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiment, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.
[0036] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.
Claims
1. A prediction method for environmental footprint based on input-output model, characterized in that, It includes the following steps: Collect the multi-regional input-output tables, income elasticity data, and environmental indicator intensities of each region for historical years; Based on the multi-regional input-output tables and environmental indicator intensities of each region for historical years, determine the multi-regional input-output matrix and the environmental indicator intensity per unit output; Combined with the prediction period, perform double logarithmic regression on the historical data of the environmental indicator intensity per unit output of each region, assign non-numeric and negative values to 0, and obtain the predicted data of the environmental indicator intensity per unit output of each region; Calculate the predicted data of the total population and the total economic volume; combined with the income elasticity data, the predicted data of the total population, and the predicted data of the total economic volume, couple the final demand items of the multi-regional input-output table, and calculate the change in the final demand of each region brought about by the change in population and consumption structure; Based on the change in the final demand of each region and the predicted data of the environmental intensity indicator per unit output of each region, construct an environmentally extended multi-regional input-output model, and calculate the environmental footprint of each region in the future under the condition that the production structure remains unchanged.
2. The prediction method of environmental footprint based on input-output model according to claim 1, characterized in that The income elasticity data includes the income elasticity coefficients of the regions and sectors corresponding to the multi-regional input-output table; the historical data of the environmental indicator intensities of each region includes carbon emissions, land use, and water resources.
3. A prediction method for environmental footprint based on an input-output model according to claim 1, characterized in that The input-output matrix includes an intermediate input matrix, a final demand matrix, a value-added vector, and a total output vector, specifically: Assume that there are R regions, and each region has N sectors. The dimension of the intermediate input matrix Z is (R*N)*(R*N), expressed as: ； where each element represents the intermediate input provided by department i in region r to department j in region s; The dimension of the final demand matrix Y is (R*N)*(R*M), where M represents the type of final demand. Combine all types of final demand into the total final demand quantity to obtain the final demand matrix Y, expressed as: ； The value-added vector is denoted as , and the gross output vector is denoted as ; The direct consumption coefficient matrix represents the input quantity of products from other departments required for the production of a unit product, which is obtained by dividing the intermediate input matrix by the total output vector and is defined as , and is expressed in matrix form as: 。 4. A prediction method for environmental footprint based on an input-output model according to claim 3, characterized in that, The environmental indicator intensity per unit output is obtained by the following steps: Invert the direct consumption coefficient matrix to obtain the complete consumption coefficient matrix, that is B =( I - A ) -1 ; I is an identity matrix with diagonal elements all being 1 and non-diagonal matrices being 0, A is the direct consumption coefficient matrix, B is the complete consumption coefficient matrix; Extract the source-separated environmental data from the environmental indicator database with a spatial resolution of 1°×1° and a time resolution of one year, obtain the gridded, annualized, and sector-separated global environmental data, and then match it to the satellite account vector of the global multi-regional input-output table; Divide the satellite account vector by the total output vector to obtain the environmental coefficient per unit output, that is, the environmental indicator intensity per unit output.
5. A prediction method for environmental footprint based on an input-output model according to claim 1, characterized in that The double logarithmic regression of the historical data of the environmental indicator intensity per unit output of each region and the obtaining of the predicted data of the environmental indicator intensity per unit output of each region are specifically as follows: Logarithmize the environmental indicator intensity per unit output for multiple historical years respectively, and assign the infinite value to 0; Based on the logarithm of the determined environmental indicator intensity per unit output and the historical years, fit a linear regression model of the average annual growth rate of the environmental indicator intensity per unit output of sector i in region s with respect to the year, as follows: ； Among them, represents s the environmental intensity of the i region department, year represents the historical year, represents the error, represents the base year s the i logarithm of the environmental intensity of the region department, represents that for each additional year, s the i average change in the logarithm of the environmental intensity of the region department; if , it indicates that the environmental intensity shows an upward trend; if , then it indicates s the i environmental intensity of the region department shows a downward trend with the increase of the year; Substitute the predicted year into the linear regression model to obtain the value of the environmental indicator intensity per unit output of the corresponding country or region in a future predicted year.
6. The prediction method of environmental footprint based on the input-output model according to claim 1, wherein, The calculation of the predicted data of the total population and the total economic volume is specifically as follows: Based on the historical total population data, current trends, and influencing factors, estimate the population quantity results of each region in a future period through a statistical model to obtain the predicted data of the total population; Based on historical economic aggregate data, the current economic status, and influencing factors, economic variables in each region for a future time period are predicted through statistical models, econometric methods, or artificial intelligence methods to obtain quantitative results and derive economic aggregate prediction data.
7. A prediction method for environmental footprint based on an input-output model according to claim 1, characterized in that The final demand items of the coupled multi-regional input-output table are used to calculate the change in final demand in each region brought about by population change and consumption structure change, specifically: Based on the historical population total data and historical economic aggregate data of the base year, the population growth rate and economic growth rate in the predicted year under different scenarios are obtained; Using the final demand items and the population growth rate in the predicted years under different scenarios, calculate s the change in the final demand caused by the change in the regional population, which is expressed by the formula: ； Among them, represents t period s the new final demand generated after the population change in the region, represents the base period s the final demand in the region, represents s the population growth rate in the region; Based on the multi-regional input-output table, determine the target region and sectors, obtain the income elasticity data for the target region and sectors, and perform logarithmic transformation on the income elasticity coefficients; based on the change ratio of GDP in future years and the income elasticity data, considering economic growth and population changes t phase s The regional final demand is expressed as: ； Among them, represents t phase s After the regional economic growth and population change, a new final demand quantity is generated. represents s Regional GDP growth rate represents s region i Income elasticity coefficient of the department Considering the impact of income growth, s the sector consumption structure of the region is represented by s the demand of each sector in the region accounts for s the proportion of the total regional demand, expressed as: ； Combined with s the change in the consumption structure of departments in the region, t period s the final demand of the region is expressed as: ； Aggregate to the regional scale, s The new final demand quantity generated by the change in the final demand structure of each sector caused by regional economic growth and population change is expressed as: 。 8. A prediction method for environmental footprint based on an input-output model according to claim 1, characterized in that, The environmentally extended multi-regional input-output model is constructed to calculate the environmental footprint of each region in the future when the production structure remains unchanged, specifically: Calculated from the perspective of the demand side t The environmental footprints of each region in the world during the 。 9. A prediction system for environmental footprint based on an input-output model, characterized in that, Applied to a method for predicting the environmental footprint based on an input-output model according to any one of claims 1-8, including a data acquisition module, a unit output environmental indicator intensity prediction module, a final demand change amount calculation module, and an environmental footprint prediction module; The data acquisition module is used to collect the multi-regional input-output table, income elasticity data, and environmental indicator intensity of each region in historical years; The unit output environmental indicator intensity prediction module is used to determine the multi-regional input-output matrix and the unit output environmental indicator intensity based on the multi-regional input-output table and the environmental indicator intensity of each region in historical years; in combination with the prediction period, the historical data of the unit output environmental indicator intensity of each region is subjected to double logarithmic regression, and non-numbers and negative values are assigned as 0 values to obtain the prediction data of the unit output environmental indicator intensity of each region; The final demand change amount calculation module is used to calculate the population total prediction data and the economic aggregate prediction data; in combination with the income elasticity data, the population total prediction data, and the economic aggregate prediction data, the final demand items of the coupled multi-regional input-output table are used to calculate the change in final demand in each region brought about by population change and consumption structure change; The environmental footprint prediction module is used to construct an environmentally extended multi-regional input-output model based on the change in final demand in each region and the prediction data of the unit output environmental intensity indicator of each region, and calculate the environmental footprint of each region in the future when the production structure remains unchanged.
10. A storage medium stores a program, characterized in that: When the program is executed by a processor, it implements a method for predicting the environmental footprint based on an input-output model according to any one of claims 1-8.