Prediction device and prediction method

Abstract

Provided is a prediction system comprising an economic growth prediction device 1, a transaction prediction device 2, and an emission amount prediction device 4. The economic growth prediction device 1 predicts future economic statistical information and a carbon emission amount through economic cycling using past economic statistical data 8, and calculates a credit investment amount on the basis of the economic statistical information and a transaction scale scenario. The transaction prediction device 2 acquires transaction proportions of forest maintenance and a high-efficiency facility using past carbon credit transaction data, calculates a forest area increased by improving the credit investment amount with an investment amount of the proportion of forest maintenance, calculates a production amount of the high-efficiency facility that is produced by improving the credit investment amount with an investment amount of the proportion of the high-efficiency facility, and calculates a carbon emission suppression amount obtained using the forest area and the high-efficiency facility. The emission amount prediction device 4 subtracts the carbon emission suppression amount from the carbon emission amount to predict a deemed carbon emission amount.

Description

Prediction device and prediction method

[0001] The present disclosure relates to a prediction device and a prediction method.

[0002] Conventionally, sustainability predictions have been made using the World3 model to predict changes in GDP due to current economic conditions (Non-Patent Document 1). The World3 model is an economic circulation model that inputs economic statistical information published in databases such as the World Bank and outputs a long-term GDP forecast.

[0003] Regarding the carbon credit system, research is also being conducted to predict assumed carbon emissions using scenarios set from the perspective of achieving environmental goals, looking at trends in the scale of credits.

[0004] “Update to the Limits to Growth: Comparing the World3 Model with Empirical Data,” [online], Internet <https: / / alzhacker.com / update-to-limits-to-growth-comparing-the-world3-model-with-empirical-data / >

[0005] The above-mentioned estimated carbon emissions forecast makes it possible to predict estimated carbon emissions (environmental targets) under the carbon credit system. However, this forecast is based on a scenario set from the perspective of achieving environmental targets independent of economic policy, and it is not possible to predict estimated carbon emissions in conjunction with the impact of the implementation of the carbon credit system on economic growth (GDP). Currently, the scale of application of the carbon credit system is small, so its impact on the economy is expected to be minor, but conventional technology cannot take into account the impact of expanding the carbon credit system in the future.

[0006] The present disclosure has been made in consideration of the above circumstances, and an object of the present disclosure is to provide a technology for predicting assumed carbon emissions linked to the impact of economic growth forecasts.

[0007] In order to achieve the above object, one aspect of the prediction system of the present disclosure includes an economic growth prediction device, a transaction prediction device, and an emissions prediction device, wherein the economic growth prediction device uses past economic statistical data to predict future economic statistical information and carbon emissions by circulating the economy, and calculates a credit investment amount based on the economic statistical information and a transaction size scenario, the transaction prediction device uses past carbon credit transaction data to obtain the transaction ratio between forest management and high-efficiency equipment, calculates the forest area increased by management with an investment amount that is the ratio of the forest management ratio to the credit investment amount, calculates the production volume of high-efficiency equipment produced with an investment amount that is the ratio of the high-efficiency equipment to the credit investment amount, and calculates the forest area and the amount of carbon emission reduction obtained by the high-efficiency equipment, and the emissions prediction device subtracts the amount of carbon emission reduction from the carbon emissions to predict the deemed carbon emissions.

[0008] One aspect of the present disclosure is a prediction method, in which an economic growth prediction device uses past economic statistical data to predict future economic statistical information and carbon emissions by circulating the economy, and calculates a credit investment amount based on the economic statistical information and a transaction size scenario, a transaction prediction device uses past carbon credit transaction data to obtain the transaction ratio between forest management and high-efficiency equipment, calculates the forest area increased by management with an investment amount that is the ratio of forest management to the credit investment amount, calculates the production volume of high-efficiency equipment produced with an investment amount that is the ratio of high-efficiency equipment to the credit investment amount, and calculates the forest area and the amount of carbon emission reduction obtained by the high-efficiency equipment, and an emission prediction device subtracts the amount of carbon emission reduction from the carbon emission amount to predict the deemed carbon emission amount.

[0009] According to the present disclosure, it is possible to provide a technology for predicting assumed carbon emissions linked to the impact of economic growth forecasts.

[0010] Fig. 1 is a diagram showing an example of the configuration of a prediction system according to this embodiment, and Fig. 2 is a diagram showing an example of the hardware configuration.

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0012] 1 is a configuration diagram showing an example of a prediction system according to this embodiment. The illustrated prediction system includes a GDP prediction device 1 (economic growth prediction device), a transaction prediction device 2, a resource quantity prediction device 3, and an emission amount prediction device 4. The prediction system also includes a predicted GDP storage unit 5, a predicted asset quantity storage unit 6, and a predicted deemed carbon emission storage unit 7.

[0013] <GDP prediction device> The GDP prediction device 1 uses past economic statistical data 8 to predict future economic statistical information and carbon emissions through economic circulation, and calculates the credit investment amount based on the economic statistical information and transaction size scenario.

[0014] In this embodiment, the GDP prediction device 1 inputs (acquires) economic statistical information for the previous year (year t) from the economic statistical data 8 (S1). Then, using the input economic statistical information, the GDP prediction device 1 executes economic circulation (production and consumption) and calculates (predicts) economic statistical information and carbon emissions for the current year (year t+1). The economic statistical information includes population, resource volume, industrial production volume, etc. Various types of economic statistical information are stored in the economic statistical data 8. For example, the World Bank Database provided by the World Bank may be used as the economic statistical data 8.

[0015] The GDP prediction device 1 stores the calculated economic statistical information in the predicted GDP storage unit 5 (S2). The predicted GDP storage unit 5 stores the economic statistical information predicted by the GDP prediction device 1. The GDP prediction device 1 repeats the economic cycle using the predicted economic statistical information for the current year (year t+1), and thereby stores economic statistical information for each point in time (year t+1, year t+2, ...) in the predicted GDP storage unit 5. The GDP prediction device 1 also sends the calculated carbon emissions to the emissions prediction device 4 (S3).

[0016] The GDP forecasting device 1 can use, for example, the World3 model (Non-Patent Document 1). The World3 model is an economic circulation model that inputs publicly available economic statistical information from databases such as the World Bank and outputs a long-term GDP forecast. The World3 model is known as a long-term economic forecasting model, as the accuracy of the output long-term forecast is subsequently compared with actual data and discussed.

[0017] For example, the GDP prediction device 1 executes economic circulation, subtracts the resource amount of the previous year using a predetermined conversion formula, and adds the industrial production value of the previous year to predict the resource amount and industrial production amount of the current year.

[0018] The GDP prediction device 1 calculates the credit investment amount based on the GDP (Gross Domestic Product) calculated by executing the economic cycle and the transaction size scenario, and sends it to the transaction prediction device 2 (S4). GDP is included in the predicted economic statistical information. The transaction size scenario in this embodiment is a scenario linked to economic policies (economic goals) such as GDP, and may be, for example, a predetermined percentage of GDP (n% of GDP). In other words, n% of GDP may be used as the credit investment amount (environmental investment).

[0019] In a credit transaction based on market principles, the price would rise in relation to the credits (additional carbon emissions), but this embodiment assumes bilateral transactions, and credit buyers can freely produce as long as they purchase the amount of carbon emissions they want at their own price. Therefore, the GDP prediction device 1 in this embodiment simply transfers the credit investment amount to the transaction prediction device 2, and does not make any corrections to subsequent production activities.

[0020] The GDP prediction device 1 acquires the developed forest area and the production volume of the highly efficient equipment from the transaction prediction device 2 (S5). The GDP prediction device 1 then corrects the resource volume and industrial production volume predicted by executing the economic cycle using the acquired forest area and the production volume of the highly efficient equipment. That is, the GDP prediction device 1 corrects the impact of the carbon credit system on the macroeconomy on industrial production volume and resource volume. The GDP prediction device 1 stores the corrected resource volume and industrial production volume in the predicted GDP storage unit 5 (S2). The economic statistical information including the resource volume and industrial production volume stored in the predicted GDP storage unit 5 is used as input for making predictions for the following year (t+2).

[0021] Specifically, investments in the carbon credit system are used to develop forests or highly efficient facilities. Regarding resource volume, the GDP prediction device 1 adds the forest area corresponding to the credit purchase amount for forest development to the resource volume predicted by implementing economic circulation. The area added at the unit price may be specified by the parameters of the forest development product under the carbon credit system.

[0022] The GDP prediction device 1 predicts industrial production volume using a predetermined conversion parameter that indicates the relationship between industrial production volume and production investment amount. Because production equipment is depreciated, for example, this conversion parameter is changed in a direction that gradually deteriorates over time, and is corrected in a direction that improves through investment in industrial equipment. In this embodiment, the GDP prediction device 1 controls carbon emissions in an improving direction in addition to industrial production volume, depending on the production volume of highly efficient equipment. The GDP prediction device 1 stores the corrected industrial production volume in the predicted asset amount storage unit 6 (S6).

[0023] The GDP prediction device 1 calculates the amount of resource consumption and sends the calculated amount of resource consumption to the emission prediction device 4 (S7). The GDP prediction device 1 reproduces the macroeconomic cycle of "investment → production → inventory → sales / profits → investment..." and calculates the amount of resource consumption required in the "production" process. The resources consumed are specified by individual parameters for each product and industrial field in which production activities are carried out. The GDP prediction device 1 calculates the amount of resource consumption using parameters set for each product and industrial field in which production activities are carried out.

[0024] <Transaction Prediction Device> The transaction prediction device 2 uses past carbon credit transaction data to obtain the transaction ratio between forest management and high-efficiency equipment, calculates the forest area increased by management with an investment amount that is the ratio of forest management to the credit investment amount, and calculates the production volume of high-efficiency equipment produced with an investment amount that is the ratio of high-efficiency equipment to the credit investment amount. The transaction prediction device 2 calculates the forest area and the carbon emission reduction amount obtained by the high-efficiency equipment.

[0025] Specifically, the transaction prediction device 2 acquires the predicted credit investment amount output from the GDP prediction device 1 (S4). The transaction prediction device 2 references carbon credit transaction data 9 (hereinafter referred to as "transaction data") (S8) and estimates the transaction ratio between forestry investment and high-efficiency capital investment. The transaction data 9 is a data set related to past carbon credit transactions. The transaction data 9 includes information related to the transaction ratio. For example, each transaction data 9 may be assigned a type indicating forestry investment or high-efficiency capital investment. The transaction prediction device 2 may use the type to calculate the total transaction amount of forestry investment and the total transaction amount of high-efficiency capital investment, and estimate the transaction ratio based on these total amounts. Furthermore, the transaction prediction device 2 may set a future ratio trend as a predetermined parameter (or scenario) for the transaction ratio calculated from the past transaction data 9.

[0026] The transaction prediction device 2 calculates the carbon emission reduction amount, forest area, and production volume (production value) of high-efficiency equipment using the transaction rate and the credit investment amount. Specifically, the transaction prediction device 2 determines the forest investment amount and the investment amount of high-efficiency equipment from the credit investment amount according to the transaction rate.

[0027] The transaction prediction device 2 then calculates the carbon emission reduction amount (the carbon reduction effect for the current year, taking into account rebates) obtained by developing the forest with the determined forest investment amount and producing the high-efficiency equipment with the determined high-efficiency equipment. The transaction prediction device 2 also calculates the forest area (change in forest area) increased by developing the forest with the forest investment amount. The transaction prediction device 2 also calculates the production volume (production value) produced by the high-efficiency equipment with the investment amount for the high-efficiency equipment. The transaction prediction device 2 then sends the increased forest area and the production volume of the high-efficiency equipment to the GDP prediction device 1 (S5). The GDP prediction device 1 accepts the forest area and the production volume of the high-efficiency equipment and feeds them back into the predicted resource volume and industrial production volume by executing the economic cycle. The transaction prediction device 2 also sends the calculated carbon emission reduction amount to the emission prediction device 4 (S9).

[0028] The transaction prediction device 2 calculates the amount of purchased carbon emissions and sends it to the GDP prediction device 1 (S5). Specifically, the transaction prediction device 2 determines the proportion of credit products (forestry maintenance, high-efficiency equipment) to be purchased based on the aforementioned transaction ratio. A carbon emission reduction amount is specified for each credit product. The transaction prediction device 2 can calculate the amount of purchased carbon emissions by assuming that the credit products in the transaction data 9 have been purchased in full. The GDP prediction device 1 sets a gap in the carbon emissions calculated by executing the economic cycle, and assumes that production will not be reduced within this gap in economic activity. The GDP prediction device 1 increases this gap by the amount of purchased carbon emissions.

[0029] <Resource Quantity Prediction Device> The resource quantity prediction device 3 predicts future resource quantities using the resource quantities acquired from the economic statistical data 8 and the resource consumption amounts included in the economic statistical information predicted by the GDP prediction device 1. In other words, the resource quantity prediction device 3 predicts changes in resource quantities from economic activities predicted by the GDP prediction device 1.

[0030] Specifically, the resource amount prediction device 3 obtains the resource amount for the previous year (year t) as an initial value from the economic statistical data 8. The resource amount prediction device 3 predicts the resource amount for the current year (year t+1) by subtracting the resource consumption amount sent from the GDP prediction device 1 from the obtained resource amount, and stores the predicted resource amount in the predicted asset amount storage unit 6 (S10). This enables the resource amount prediction device 3 to predict the resource amount corrected by the transaction prediction device 2 to take into account the influence of carbon credits.

[0031] The predicted asset amount storage unit 6 stores the resource amount predicted by the resource amount prediction device 3 and the industrial production amount predicted by the GDP prediction device 1. The GDP prediction device 1 repeats the economic cycle using the predicted economic statistical information for the current year (year t+1), and the resource amount prediction device 3 repeats prediction of the resource amount using the predicted resource amount, so that the predicted asset amount storage unit 6 stores the industrial production amount and resource amount for each point in time (year t+1, year t+2, ...).

[0032] <Emission Amount Prediction Device> The emission amount prediction device 4 subtracts the carbon emission suppression amount output from the transaction prediction device 2 from the carbon emission amount output from the GDP prediction device 1 to predict the assumed carbon emission amount.

[0033] Specifically, the emission amount prediction device 4 calculates the assumed carbon emission amount using the following formula, and stores the calculated estimated assumed carbon emission amount in the storage unit 7 (S11).

[0034] Deemed carbon emissions = carbon emissions - carbon emission reductions

[0035] The emission prediction device 4 calculates the carbon emission amount deemed to have been reduced by carbon reduction by subtracting the amount of carbon emission reduction achieved by the carbon credit product from the carbon emission amount. In this embodiment, by defining in advance a transaction scale scenario (for example, n% of GNP) that assumes an expansion in the scale of credit trading volume based on a long-term GDP forecast, it is possible to predict the deemed carbon emission amount and carbon credit trading volume that will occur in that scenario.

[0036] The GDP prediction device 1 repeats the economic cycle using the predicted economic statistical information for the current year (t+1 year), and the emissions prediction device 4 repeatedly predicts the assumed carbon emissions, so that the predicted assumed carbon emissions memory unit 7 stores the assumed carbon emissions at each point in time (t+1 year, t+2 year, ...).

[0037] Companies engaged in production activities (carbon emissions) purchase carbon credits according to the transaction scale scenario and purchase the corresponding amount of carbon emission reductions. This is considered to have reduced carbon emissions, and the deemed carbon emissions, calculated by subtracting the purchased amount of carbon emission reductions from the carbon emissions, are considered to be the actual emissions.

[0038] <Long-Term Prediction> The GDP prediction device 1, the transaction prediction device 2, and the emission amount prediction device 4 repeat the above-mentioned processes, thereby making it possible to predict future long-term changes in GDP and assumed carbon emissions.

[0039] Specifically, the GDP prediction device 1 predicts changes in economic statistical information and carbon emissions for a specified period by repeating the economic cycle using the predicted economic statistical information, and calculates the amount of credit investment at each point in time (year t, year t+1, year t+2, ...) based on the predicted economic statistical information and transaction size scenario at each point in time during the specified period.

[0040] The transaction prediction device 2 calculates the forest area increased by maintaining the forest with an investment amount that is the ratio of the credit investment amount to the forest maintenance amount at each point in time, calculates the production volume of high-efficiency equipment produced with an investment amount that is the ratio of the credit investment amount to the high-efficiency equipment amount, and calculates the increased forest area at each point in time and the amount of carbon emission reduction obtained by the produced high-efficiency equipment.

[0041] The emission prediction device subtracts the carbon emission reduction amount at each point in time from the carbon emission amount predicted by the economic growth prediction device at each point in time, calculates the deemed carbon emission amount at each point in time, and predicts changes in the deemed carbon emission amount for the specified period.

[0042] Furthermore, the resource amount prediction device 3 predicts changes in resource amounts for the predetermined period by using the resource amounts predicted at each point in time and the resource consumption amounts included in the economic statistical information predicted by the GDP prediction device 1. This makes it possible to predict changes in resource amounts.

[0043] The transaction ratio calculated by the transaction prediction device 2 may be a fixed ratio calculated using the carbon credit transaction data 9, or a numerical value for each year t+1 may be defined as a scenario parameter. In addition, if the forest becomes too overgrown to be maintained, a supply and demand model may be used that reduces the transaction ratio for forest maintenance.

[0044] As a result, the prediction system of this embodiment can also predict changes in the amount of capital held by a country. The amount of capital is the total amount of economic assets such as resources (forests, fossil fuels, etc.), population, labor force, product inventory, and cash. In addition, a state in which a country's capital amount is increasing can be considered a sustainable state.

[0045] The prediction system of the present embodiment described above includes a GDP prediction device 1, a transaction prediction device 2, and an emissions prediction device 4. The GDP prediction device 1 uses past economic statistical data 8 to predict future economic statistical information and carbon emissions by circulating the economy, and calculates the credit investment amount based on the economic statistical information and a transaction size scenario. The transaction prediction device 2 uses past carbon credit transaction data 9 to obtain the transaction ratio between forest management and high-efficiency equipment, calculates the forest area increased by managing the forest with an investment amount that is the ratio of the forest management ratio to the credit investment amount, calculates the production volume of the high-efficiency equipment produced with an investment amount that is the ratio of the high-efficiency equipment to the credit investment amount, and calculates the forest area and the amount of carbon emission reduction achieved by the high-efficiency equipment. The emissions prediction device 4 subtracts the amount of carbon emission reduction from the amount of carbon emission to predict the deemed carbon emission.

[0046] As a result, in this embodiment, it is possible to predict deemed carbon emissions linked to the impact of economic growth forecasts (GDP, economic indicators). For example, deemed carbon emissions can be predicted with high accuracy according to a scenario in which the carbon credit system is expanded (e.g., n% of GDP uses the carbon credit system).

[0047] In this embodiment, by inputting the impact of the carbon credit system into the GDP prediction device 1 (economic circulation model), it is possible to predict not only carbon emissions but also changes in GDP and the assets held by the country (forests, population, economic output).

[0048] In this embodiment, by taking into account the impact of the carbon credit system, it is possible to predict not only carbon emissions, which are a climate change factor, but also the impact on economic growth rates and changes in national power (the amount of assets held by the country), not only in terms of values ​​at a snapshot 50 years from now, for example, but also whether the country is progressing in a sustainable state (a state in which the country's capital is increasing) in the process.

[0049] The GDP prediction device 1, transaction prediction device 2, resource amount prediction device 3, and emission amount prediction device 4 described above can be implemented, for example, by a general-purpose computer system such as that shown in FIG. 2. The illustrated computer system includes a CPU (Central Processing Unit, processor) 901, a memory 902, a storage 903 (HDD: Hard Disk Drive, SSD: Solid State Drive), a communication device 904, an input device 905, and an output device 906. The memory 902 and the storage 903 are storage devices. In this computer system, the CPU 901 executes a predetermined program loaded onto the memory 902, thereby realizing the functions of the GDP prediction device 1, transaction prediction device 2, resource amount prediction device 3, and emission amount prediction device 4.

[0050] The GDP prediction device 1, the transaction prediction device 2, the resource amount prediction device 3, and the emission amount prediction device 4 may be implemented on a single computer or multiple computers. The GDP prediction device 1, the transaction prediction device 2, the resource amount prediction device 3, and the emission amount prediction device 4 may be virtual machines implemented on a computer. The programs for the GDP prediction device 1, the transaction prediction device 2, the resource amount prediction device 3, and the emission amount prediction device 4 can be stored on a computer-readable recording medium such as a HDD, SSD, USB (Universal Serial Bus) memory, CD (Compact Disc), or DVD (Digital Versatile Disc), or can be distributed via a network. The computer-readable recording medium is, for example, a non-transitory recording medium.

[0051] The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the present disclosure.

[0052] 1: GDP prediction device (economic growth prediction device) 2: Transaction prediction device 3: Resource amount prediction device 4: Emission amount prediction device 5: Predicted GDP storage unit 6: Predicted asset amount storage unit 7: Predicted assumed carbon emission amount storage unit 8: Economic statistics data 9: Carbon credit transaction data

Claims

1. A prediction system comprising an economic growth prediction device, a transaction prediction device, and an emissions prediction device, wherein the economic growth prediction device uses past economic statistical data to predict future economic statistical information and carbon emissions by circulating the economy, and calculates a credit investment amount based on the economic statistical information and a transaction size scenario, the transaction prediction device uses past carbon credit transaction data to obtain the transaction ratio between forest management and high-efficiency equipment, calculates the forest area increased by managing the forest with an investment amount that is the ratio of the credit investment amount to the forest management ratio, and calculates the production volume of the high-efficiency equipment produced with an investment amount that is the ratio of the high-efficiency equipment to the credit investment amount, and calculates the forest area and the amount of carbon emission reduction achieved by the high-efficiency equipment, and the emissions prediction device subtracts the amount of carbon emission reduction from the carbon emissions to predict assumed carbon emissions.

2. A prediction system as described in claim 1, comprising a resource amount prediction device that predicts future resource amounts using resource amounts obtained from the economic statistical data and resource consumption amounts included in the economic statistical information.

3. The prediction system described in claim 1, wherein the economic growth prediction device predicts changes in economic statistical information and carbon emissions for a specified period by repeating economic cycles using the predicted economic statistical information, and calculates the credit investment amount for each time point based on the predicted economic statistical information and transaction size scenario at each time point during the specified period; the transaction prediction device calculates the forest area increased by maintaining the forest at an investment amount that is the proportion of the credit investment amount at each time point, and calculates the production volume of highly efficient equipment produced at an investment amount that is the proportion of high efficiency equipment produced in addition to the credit investment amount, thereby calculating the increased forest area at each time point and the amount of carbon emission reduction achieved by the highly efficient equipment produced; and the emission prediction device subtracts the amount of carbon emission reduction at each time point from the amount of carbon emission predicted by the economic growth prediction device at each time point, calculates the deemed carbon emission at each time point, and predicts changes in deemed carbon emission for the specified period.

4. An economic growth forecasting device uses past economic statistical data to predict future economic statistical information and carbon emissions by circulating the economy, and calculates a credit investment amount based on the economic statistical information and a transaction size scenario; a transaction forecasting device uses past carbon credit transaction data to obtain the transaction ratio between forest management and high-efficiency equipment, calculates the forest area increased by managing with an investment amount that is the ratio of forest management to the credit investment amount, and calculates the production volume of high-efficiency equipment produced with an investment amount that is the ratio of high-efficiency equipment to the credit investment amount; calculates the forest area and the amount of carbon emission reduction obtained by the high-efficiency equipment; and an emission forecasting device subtracts the amount of carbon emission reduction from the carbon emission amount to predict assumed carbon emissions.

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