Prediction apparatus, prediction method, and prediction program

Abstract

This prediction device (100) comprises an influence prediction unit (136), a GDP prediction adjustment unit (137), and a happiness degree prediction unit (135). The influence prediction unit (136) predicts an influence on the growth of per capita GDP on the basis of a predicted value of a subjective degree of happiness in the past. The GDP prediction adjustment unit (137) adjusts a predicted value of the per capita GDP on the basis of the influence predicted by the influence prediction unit (136). The happiness degree prediction unit (135) calculates a predicted value of the subjective degree of happiness on the basis of the predicted value of the per capita GDP adjusted by the GDP prediction adjustment unit (137).

Description

Prediction device, prediction method, and prediction program

[0001] The present invention relates to a prediction device, a prediction method, and a prediction program.

[0002] Many studies have been conducted to evaluate what factors influence subjective well-being. While most of the previous studies have examined and evaluated the relationship between actually observed current or past subjective well-being (for example, life satisfaction by region) and other social indicators, there has not been enough research into future subjective well-being.

[0003] Previous studies on well-being have suggested that subjective well-being can be predicted using the logarithm of per capita GDP (Gross Domestic Product), but this has the problem of insufficient accuracy. Therefore, a technology has been proposed that enables future predictions of subjective well-being by combining a subjective well-being estimation model using multiple regression analysis with the objective indicator predictions of an existing integrated assessment model (see Non-Patent Document 1).

[0004] Research has also shown that productivity increases by 12% when subjective well-being increases by one level on a scale of 0 to 10 (see Non-Patent Document 2). Research has also shown that GDP growth rates are affected by annual temperature increases, and that the effect of temperature on GDP growth rates is expressed as a quadratic function of the average annual temperature, with the optimum (loss = 0) occurring when the average annual temperature is 13°C (see Non-Patent Documents 3 to 5). Additionally, research has shown that technological progress and energy demand are factors that determine GDP growth in the SSP scenario, a socioeconomic scenario (see Non-Patent Document 6).

[0005] Nihongi, Nobuyoshi, and Maruyoshi, Masahiro, "Constructing a Future Prediction Model of Subjective Life Satisfaction by Country," Institute of Electronics, Information and Communication Engineers, 2023 General Conference, Fundamentals and Boundaries / NOLTA Proceedings, p. 153, 2023. Clement S. Bellet et al., "Does Employee Happiness Have an Impact on Productivity?", Management Science, 2023, Vol. 70, No. 3, [Retrieved July 1, 2024], Internet <URL: https: / / pubsonline.informs.org / doi / 10.1287 / mnsc.2023.4766> Marshall Burke et al., "Global non-linear effect of temperature on economic production," Nature, 527, 235-239 (2015), [Retrieved July 1, 2024], Internet <URL: https: / / www.nature.com / articles / nature15725> Anselm Schultes et al. "Economic damages from ongoing climate change imply deeper near-term emission cuts," Environ. Res. Lett. 16 (2021) 104053, [Retrieved July 1, 2024], Internet <URL: https: / / iopscience.iop.org / article / 10.1088 / 1748-9326 / ac27ce> Anselm Schultes et al. "Economic damages from ongoing climate change imply deeper near-term emission cuts," Munich Personal RePEc Archive, 2020, MPRA Paper No. 103655, [Retrieved July 1, 2024], Internet <URL: https: / / mpra.ub.uni-muenchen.de / 103655 / 1 / MPRA_paper_103655.pdf〉Dellink, Rob et al, "Long-term economic growth projections in the shared socioeconomic pathways", Global Environmental Change, 42 (2017) 200-214, [Retrieved July 1, 2024], Internet〈URL: https: / / www.sciencedirect.com / science / article / abs / pii / S0959378015000837〉.

[0006] However, the above-mentioned conventional techniques for predicting future subjective well-being do not consider the impact of changes in subjective well-being on changes in per capita GDP, and there is room for improvement.

[0007] Therefore, in order to solve the above problems and achieve the object, the prediction device of the present invention comprises an impact prediction unit, a GDP prediction adjustment unit, and a happiness prediction unit. The impact prediction unit predicts the impact on per capita GDP growth based on predicted values ​​of past subjective happiness. The GDP prediction adjustment unit adjusts the predicted value of per capita GDP based on the impact predicted by the impact prediction unit. The happiness prediction unit calculates a predicted value of subjective happiness based on the predicted value of per capita GDP adjusted by the GDP prediction adjustment unit.

[0008] The present invention has an effect of improving the prediction accuracy of subjective well-being.

[0009] FIG. 1 is a diagram showing an overview of prediction of subjective well-being by region according to this embodiment. FIG. 2 is a diagram showing an example of the device configuration of a prediction device according to the first embodiment. FIG. 3 is a diagram showing an overview of socioeconomic scenarios according to the first embodiment. FIG. 4 is a diagram showing an example of a prediction graph of subjective well-being by socioeconomic scenario according to the first embodiment. FIG. 5 is a diagram showing an example of the device configuration of a prediction device according to the second embodiment. FIG. 6 is a diagram showing an example of a flowchart of a prediction method according to the first embodiment. FIG. 7 is a diagram showing an example of a flowchart of a prediction method according to the second embodiment. FIG. 8 is a diagram showing an example of a computer on which the prediction device according to this embodiment is realized.

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Note that the embodiments are not limited to the following description.

[0011] [1. Overview] The prediction device 100 of this embodiment calculates a predicted value of per capita GDP and calculates a predicted value of subjective well-being based on the predicted value of per capita GDP. Subjective well-being is life satisfaction, but is not limited to this example and may include emotional well-being, hedonistic well-being, etc. instead of or in addition to life satisfaction.

[0012] Because per capita GDP is also affected by subjective well-being and temperature, the prediction device 100 predicts the impact on per capita GDP growth based on the predicted values ​​of past subjective well-being and average temperatures, and adjusts the predicted value of per capita GDP based on the predicted impact.The prediction device 100 then calculates a predicted value of subjective well-being based on the predicted value of adjusted per capita GDP, the predicted values ​​of social indicators, and the predicted value of climate.

[0013] This enables the prediction device 100 to improve the accuracy of prediction of subjective well-being, and to more appropriately utilize subjective well-being as an index when, for example, considering the formulation of measures or policies.

[0014] Here, a method for calculating predicted values ​​of subjective well-being by region (e.g., by country) using the prediction device 100 will be described with reference to Fig. 1. Fig. 1 is a diagram showing an overview of prediction of subjective well-being by region according to this embodiment.

[0015] As shown in FIG. 1, the prediction device 100 performs GDP prediction processing, social index prediction processing, climate index prediction processing, average temperature prediction processing, impact prediction processing, GDP prediction adjustment processing, and happiness prediction processing.

[0016] In the GDP prediction process, the prediction device 100 calculates a predicted value of per capita GDP for each region for each socioeconomic scenario based on, for example, actual values ​​of per capita GDP for each region.In the social indicator prediction process, the prediction device 100 calculates a predicted value of social indicator for each region for each socioeconomic scenario based on, for example, actual values ​​of social indicator for each region and predicted values ​​of per capita GDP for each region adjusted in the GDP prediction adjustment process.

[0017] In the climate index prediction process, the prediction device 100 calculates predicted values ​​of regional climate indices for each socioeconomic scenario, for example, based on actual values ​​of regional climate indices.

[0018] In the average temperature prediction process, the prediction device 100 calculates a predicted value of the average temperature for each region for each socioeconomic scenario based on the predicted value of the social index for each region (e.g., predicted value of greenhouse gas emissions) calculated in the social index prediction process. The predicted value of the average temperature for each region is, for example, a predicted value of the annual average temperature for each region, but is not limited to this example.

[0019] In the impact prediction process, the prediction device 100 predicts the impact of subjective well-being and average temperature on the growth of per capita GDP by region based on the predicted value of past subjective well-being calculated by the well-being prediction process and the predicted value of average temperature calculated by the average temperature prediction process.

[0020] In the GDP forecast adjustment process, the prediction device 100 adjusts the predicted value of per capita GDP by region calculated in the GDP forecast process for each regional scenario based on the impact on the growth of per capita GDP by region predicted by the impact forecast process.

[0021] Then, in the happiness prediction process, the prediction device 100 calculates a predicted value of regional subjective happiness for each socioeconomic scenario based on the predicted value of per capita GDP by region after adjustment in the GDP prediction adjustment process, the predicted value of social indicators by region calculated in the social indicator prediction process, and the predicted value of climate indicators by region calculated in the climate indicator prediction process.

[0022] The predicted value of per capita GDP after adjustment in the GDP forecast adjustment process takes into account the influence of subjective well-being, average temperature, and other factors. Furthermore, because subjective well-being is affected by, for example, environmental impacts, future predictions of subjective well-being take into account measures to combat rising temperatures and environmental impacts in addition to economic development. Therefore, by using the predicted value of per capita GDP after adjustment in the GDP forecast adjustment process in the well-being prediction process, the accuracy of the predicted value of subjective well-being can be improved, and subjective well-being can be more appropriately used as an indicator when considering the formulation of measures and policies.

[0023] 2. First Embodiment A first embodiment implemented by the prediction device 100 of this embodiment will now be described. The first embodiment is an embodiment that includes processing for predicting the impact on the predicted value of regional GDP per capita based on the predicted value of past subjective well-being by region, and adjusting the predicted value of regional GDP per capita based on the predicted impact.

[0024] The region may be, for example, by country, but is not limited to such an example. For example, the region may be by a combination of multiple countries (for example, by economic block, by continent, etc.), or by city, town, or village.

[0025] 2.1. Configuration of the Prediction Device 100 An example configuration of the prediction device 100 according to this embodiment will now be described with reference to Fig. 2. Fig. 2 is a diagram illustrating an example of the device configuration of the prediction device 100 according to the first embodiment. As shown in Fig. 2, the prediction device 100 includes a communication unit 110, a storage unit 120, and a processing unit 130.

[0026] (Communication Unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC) or the like, and controls communication via a telecommunication line such as a local area network (LAN), the internet, etc. The communication unit 110 is connected to a network via a wired or wireless connection as necessary, and can transmit and receive information bidirectionally.

[0027] (Storage Unit 120) The storage unit 120 stores data and programs used for various processes by the processing unit 130. The storage unit 120 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

[0028] As shown in FIG. 2 , the storage unit 120 includes a GDP actual value storage unit 121 , a social index actual value storage unit 122 , a climate actual value storage unit 123 , a forecast history storage unit 124 , and a socioeconomic scenario information storage unit 125 .

[0029] (GDP actual value storage unit 121) The GDP actual value storage unit 121 stores actual values ​​of per capita GDP. The actual values ​​of per capita GDP are, for example, actual values ​​by region, but are not limited to this example. For example, the GDP actual value storage unit 121 stores actual values ​​of per capita GDP before 2020 as actual values ​​of per capita GDP, but are not limited to this example.

[0030] (Social index actual value storage unit 122) The social index actual value storage unit 122 stores actual values ​​of social indicators. The actual values ​​of social indicators are, for example, actual values ​​by region, but are not limited to this example. For example, the social index actual value storage unit 122 stores actual values ​​of social indicators before 2020 as actual values ​​of social indicators, but are not limited to this example.

[0031] Social indicators include, but are not limited to, at least one of greenhouse gas (e.g., carbon dioxide, methane, etc.) emissions, black carbon emissions, water consumption, food prices, food production, energy prices, and land use patterns.

[0032] (Climate actual value storage unit 123) The climate actual value storage unit 123 stores actual values ​​of climate indices. The actual values ​​of climate indices are, for example, actual values ​​by region, but are not limited to this example. For example, the climate actual value storage unit 123 stores actual values ​​of climate indices before 2020 as the actual values ​​of climate indices, but are not limited to this example.

[0033] Examples of climate indicators include, but are not limited to, average temperature, sea level rise, and weather impact indicators (e.g., changes in precipitation patterns, frequency of occurrence of extreme weather events, etc.).

[0034] (Prediction History Storage Unit 124) The prediction history storage unit 124 stores various predicted values ​​calculated by the processing unit 130. The predicted values ​​calculated by the processing unit 130 are predicted values ​​for each socioeconomic scenario by region, such as a predicted value of pre-adjustment per capita GDP, a predicted value of adjusted per capita GDP, a predicted value of social indicators, a predicted value of climate indicators, a predicted value of average temperature, and a predicted value of subjective well-being.

[0035] The prediction history storage unit 124 can store the regional prediction values ​​calculated by the processing unit 130 in any format. For example, the prediction history storage unit 124 may store the regional prediction values ​​in the form of numerical values, text, graphs, mathematical expressions, diagrams, etc.

[0036] (Socioeconomic scenario information storage unit 125) The socioeconomic scenario information storage unit 125 stores information related to socioeconomic scenarios. The socioeconomic scenarios are, for example, Shared Socioeconomic Pathways (SSP) scenarios. The SSP scenarios are commonly used in climate change analyses and represent assumptions about the future socioeconomy.

[0037] Fig. 3 is a diagram showing an overview of socioeconomic scenarios according to the first embodiment. As shown in Fig. 3, the SSP scenarios are divided into five scenarios representing images of society, namely SSP1, SSP2, SSP3, SSP4, and SSP5, which are classified along two axes: "difficulty of climate change mitigation measures" and "difficulty of climate change adaptation measures."

[0038] SSP1 is an SSP scenario with a "sustainable" society image, SSP2 is an SSP scenario with a "moderate" society image, SSP3 is an SSP scenario with a "regional division" society image, SSP4 is an SSP scenario with a "disparity" society image, and SSP5 is an SSP scenario with a "fossil fuel dependence" society image.

[0039] The items for each social vision include "population," "economy," "urbanization," "education," "technological innovation," "fossil fuel constraints," and "environment." For example, an SSP of "1" indicates a "sustainable" social vision, a "relatively low" population, "developed countries: medium, developing countries: high" economy, "high" urbanization, "high" education, "rapid" technological innovation, "fossil fuel-free preference" for fossil fuel constraints, and "efforts for continuous improvement" for the environment.

[0040] The socioeconomic scenario information storage unit 125 stores, as information related to socioeconomic scenarios, information used when various predicted values ​​for each socioeconomic scenario (e.g., SSP1, SSP2, SSP3, SSP4, SSP5) are calculated by the processing unit 130. Note that the socioeconomic scenarios are not limited to SSP scenarios.

[0041] (Processing Unit 130) The processing unit 130 has an internal memory for temporarily storing programs that define various processing procedures and processing data, and is realized by, for example, electronic circuits such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), or integrated circuits such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0042] As shown in Figure 2, the processing unit 130 has a reception unit 131, a GDP prediction unit 132, a social index value prediction unit 133, a climate index value prediction unit 134, a happiness prediction unit 135, an impact prediction unit 136, a GDP prediction adjustment unit 137, and a provision unit 138.

[0043] (Receiving Unit 131) The receiving unit 131 receives a request for prediction of a subjective well-being via the communication unit 110. The prediction request includes, for example, information indicating a prediction target period, which is a period to be predicted.

[0044] Furthermore, the reception unit 131 receives various actual results values ​​via the communication unit 110, and stores the received various actual results values ​​in the storage unit 120. For example, the reception unit 131 receives an actual value of per capita GDP, and stores the received actual value of per capita GDP in the GDP actual value storage unit 121.

[0045] The reception unit 131 also receives actual values ​​of social indicators and stores the received actual values ​​of social indicators in the social indicator actual value storage unit 122. The reception unit 131 also receives actual values ​​of climate indicators and stores the received actual values ​​of climate indicators in the climate actual value storage unit 123.

[0046] (GDP prediction unit 132) The GDP prediction unit 132 performs GDP prediction processing to calculate a predicted value of per capita GDP by region for each socioeconomic scenario. The GDP prediction unit 132 stores the calculated predicted value of per capita GDP by region and socioeconomic scenario in the prediction history storage unit 124.

[0047] For example, in the GDP prediction process, the GDP prediction unit 132 calculates predicted values ​​of per capita GDP by region for each socioeconomic scenario based on the actual values ​​of per capita GDP stored in the GDP actual value storage unit 121.

[0048] In addition, in the GDP prediction process, the GDP prediction unit 132 calculates the predicted value of per capita GDP by region for each unit period (e.g., one year) in the prediction period indicated in the prediction request received by the reception unit 131, for each socioeconomic scenario.

[0049] (Social index value prediction unit 133) The social index value prediction unit 133 performs a social index prediction process to calculate predicted values ​​of social indexes by region for each socioeconomic scenario. The social index value prediction unit 133 stores the calculated predicted values ​​of social indexes by region and socioeconomic scenario in the prediction history storage unit 124.

[0050] For example, in the social index prediction process, the social index value prediction unit 133 calculates predicted values ​​of social indicators for each socioeconomic scenario based on the actual values ​​of social indicators stored in the social index actual value storage unit 122 and the past predicted values ​​of per capita GDP after adjustment by the GDP prediction adjustment unit 137. Note that the predicted value of per capita GDP used to calculate the predicted value of subjective well-being for the first year of the prediction period is the predicted value of per capita GDP that has not been adjusted by the GDP prediction adjustment unit 137.

[0051] The Integrated Assessment Model (IAM) is used in the social index prediction process performed by the social index value prediction unit 133. The Integrated Assessment Model is software that models the interrelationships between climate, energy, land use, economy, etc. for a country or region, and performs scenario analysis.

[0052] Examples of integrated assessment models include, but are not limited to, the Global Change Assessment Model (GCAM), the Regional Model of Investments and Development (REMIND), and the Asia-Pacific Integrated Model (AIM).

[0053] The social indicators for which predicted values ​​are calculated by the social indicator value prediction unit 133 include, for example, at least one of greenhouse gas (e.g., carbon dioxide, methane gas, etc.) emissions, black carbon emissions, water consumption, food prices, food production, energy prices, and land use patterns, but are not limited to such examples.

[0054] In addition, in the social index prediction process, the social index value prediction unit 133 calculates predicted values ​​of social indexes by region for each unit period (e.g., one year) in the prediction target period indicated in the prediction request received by the receiving unit 131, for each socioeconomic scenario.

[0055] (Climate index value prediction unit 134) The climate index value prediction unit 134 performs a climate prediction process to calculate predicted values ​​of regional climate indices for each socioeconomic scenario. The climate index value prediction unit 134 stores the calculated predicted values ​​of regional climate indices for each socioeconomic scenario in the prediction history storage unit 124.

[0056] In the climate prediction process, the climate index value prediction unit 134 calculates predicted values ​​of regional climate indices for each socioeconomic scenario based on the actual values ​​of the climate indices stored in the actual climate value storage unit 123 .

[0057] In the climate forecasting process, predicted values ​​of climate indices are calculated using, for example, a climate model, which is a model that predicts climate indices such as average temperature, sea level rise, and weather impact indices (e.g., changes in precipitation patterns, frequency of occurrence of extreme weather events, etc.), but is not limited to these examples.

[0058] The climate model is, for example, but not limited to, the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC). For example, the climate model may be a Finite Amplitude Impulse Response (FAIR) model.

[0059] In addition, in the climate prediction process, the climate index value prediction unit 134 calculates predicted values ​​of regional climate indexes for each unit period (e.g., one year) in the prediction period indicated in the prediction request received by the reception unit 131, for each socioeconomic scenario.

[0060] (Happiness prediction unit 135) The happiness prediction unit 135 performs happiness prediction processing to calculate region-specific subjective happiness levels for each socioeconomic scenario. The happiness prediction unit 135 stores the calculated region-specific and socioeconomic scenario-specific subjective happiness levels in the prediction history storage unit 124.

[0061] The happiness prediction unit 135 performs happiness prediction processing to calculate regional subjective happiness levels for each socioeconomic scenario based on the predicted value of per capita GDP after adjustment by the GDP prediction adjustment unit 137, the predicted value of the social index calculated by the social index value prediction unit 133, and the predicted value of the climate index calculated by the climate index value prediction unit 134.

[0062] The well-being prediction unit 135 uses, for example, a subjective well-being estimation model to calculate regional subjective well-being for each socioeconomic scenario based on the predicted values ​​of adjusted per capita GDP, predicted values ​​of social indicators, and predicted values ​​of climate indicators. Note that the predicted value of per capita GDP used to calculate the predicted value of subjective well-being for the first year of the prediction period is the predicted value of per capita GDP that has not been adjusted by the GDP prediction adjustment unit 137.

[0063] The subjective well-being estimation model is a model that uses, for example, predicted values ​​of adjusted per capita GDP, predicted values ​​of social indicators, and predicted values ​​of climate indicators as explanatory variables and subjective well-being as the objective variable. Subjective well-being is expressed as life satisfaction, but is not limited to this example and may include emotional well-being, hedonistic well-being, etc. instead of or in addition to life satisfaction.

[0064] The subjective well-being estimation model may be, for example, a multiple regression model, but is not limited to this example. For example, the subjective well-being estimation model may be a multivariate regression analysis model, a neural network model, or other models.

[0065] The subjective happiness output from the subjective happiness estimation model is expressed, for example, as life satisfaction, but is not limited to this example and may be, for example, emotional happiness or hedonistic happiness, or may be a value that combines two or more of life satisfaction, emotional happiness, and hedonistic happiness.

[0066] As described above, the happiness prediction unit 135 can calculate the predicted value of subjective well-being using the predicted value of per capita GDP after adjustment by the GDP prediction adjustment unit 137. The predicted value of per capita GDP after adjustment is a predicted value that has been adjusted based on the predicted value of past subjective well-being. Therefore, the happiness prediction unit 135 can calculate the predicted value of subjective well-being with higher accuracy.

[0067] (Impact prediction unit 136) The impact prediction unit 136 predicts the impact on per capita GDP growth by region for each socioeconomic scenario based on the predicted values ​​of past subjective well-being. The impact prediction unit 136 stores information indicating the predicted impact on per capita GDP growth by region and socioeconomic scenario in the prediction history storage unit 124.

[0068] For example, the impact prediction unit 136 predicts the economic impact for fiscal year t for each socioeconomic scenario using the predicted or actual values ​​of subjective well-being for fiscal year t-1 by region. For example, if the prediction period specified in the prediction request received by the receiving unit 131 is from 2021 to 2100, "t" is represented as t∈{2021, 2022, 2023, ..., 2100}.

[0069] The impact prediction unit 136 uses, as a model for predicting the economic impact in fiscal year t, a model in which the impact of one level in subjective well-being corresponds to 12% of labor productivity in the following year. For example, if the rate of change in labor productivity is treated as being equal to the rate of change in per capita GDP and the impact on the growth of per capita GDP is x(t), the impact on the growth of per capita GDP x(t) can be expressed by the following formula (1): x(t) = 0.12 × (H t-1 -H0) ...(1)

[0070] In the above formula (1), "H t-1 " indicates the subjective well-being in fiscal year t-1, and "H0" indicates the subjective well-being in the first year, which serves as the reference. The subjective well-being in the first year is the subjective well-being in the first year of the prediction period. Furthermore, if the first year of the prediction period is 2021 and fiscal year t is 2022, fiscal year t-1 is 2021.

[0071] The impact x(t) expressed in the above formula (1) is the impact on the growth rate of GDP per capita, but is not limited to this example, and the model for predicting the economic impact in fiscal year t is not limited to a model in which the impact of one level on subjective well-being corresponds to 12% of labor productivity in the following year.

[0072] (GDP forecast adjustment unit 137) The GDP forecast adjustment unit 137 adjusts the forecast value of per capita GDP for year t calculated by the GDP forecast unit 132, based on the impact on the growth of per capita GDP for year t predicted by the impact forecast unit 136.

[0073] The adjustment of the predicted value of per capita GDP for year t by the GDP forecast adjustment unit 137 is an adjustment of the predicted value of per capita GDP for year t by region, and is performed for each socioeconomic scenario.

[0074] The unadjusted forecast for GDP per capita is "g t " and the adjusted per capita GDP is "g' t ", the predicted value of per capita GDP after adjustment by the GDP prediction adjustment unit 137 is expressed by the following formula (2): g' t =(1+0.12×(H t-1 -H0)) × g t ... (2)

[0075] The GDP forecast adjustment unit 137 calculates a forecast value of adjusted per capita GDP by region for each unit period in the forecast period, for example, where t represents each unit period in the forecast period indicated in the forecast request received by the reception unit 131. For example, if the forecast period is from 2021 to 2100, t represents each year from 2021 to 2100, and adjusts the adjusted per capita GDP by region.

[0076] (Providing unit 138) The providing unit 138 provides prediction information including information indicating predicted values ​​of subjective well-being by region and by socioeconomic scenario calculated by the well-being prediction unit 135. The providing unit 138 provides the prediction information to the user of the terminal device that sent the prediction request, by, for example, transmitting the prediction information to the terminal device that sent the prediction request received by the receiving unit 131 via the communication unit 110.

[0077] For example, the providing unit 138 provides, as reservation information, information including information showing a prediction graph including predicted values ​​for each year of subjective happiness in a specific region calculated by the happiness prediction unit 135 for each socioeconomic scenario.

[0078] Fig. 4 is a diagram showing an example of a forecast graph of subjective well-being by socioeconomic scenario according to the first embodiment. The forecast graph shown in Fig. 4 shows changes in the forecast value of Japan's subjective well-being from 2021 to 2100 for each of the SSP scenarios (SSP1 to SSP5) as socioeconomic scenarios, as a forecast of subjective well-being by region.

[0079] 3. Second Embodiment A first embodiment implemented by the prediction device 100 of this embodiment will be described. The second embodiment predicts the impact on per capita GDP based on past predicted values ​​of weather in addition to past predicted values ​​of subjective well-being.

[0080] 5 is a diagram illustrating an example of the device configuration of the prediction device 100 according to the second embodiment. As illustrated in FIG. 5, the prediction device 100 according to the second embodiment includes a communication unit 110, a storage unit 120, and a processing unit 130.

[0081] The prediction device 100 according to the second embodiment can be realized with the same configuration and functions as the prediction device 100 according to the first embodiment. Therefore, in this section, only functional units that are different between the first embodiment and the second embodiment will be described, and other descriptions will be omitted.

[0082] (Prediction history storage unit 124) The prediction history storage unit 124 stores the predicted value of the average temperature calculated by the processing unit 130 in addition to the predicted value of per capita GDP, the predicted value of the social index, the predicted value of the climate index, the predicted value of the average temperature, the predicted value of the subjective well-being, etc.

[0083] (Processing unit 130) As shown in FIG. 5 , the processing unit 130 includes a reception unit 131, a GDP prediction unit 132, a social index value prediction unit 133, a climate index value prediction unit 134, a happiness prediction unit 135, an impact prediction unit 136, a GDP prediction adjustment unit 137, and a provision unit 138, as well as an average temperature prediction unit 139.

[0084] (Average Temperature Prediction Unit 139) The average temperature prediction unit 139 predicts the average temperature T ｔ The average temperature prediction unit 139 calculates the predicted value of the average temperature T for year t for each region and socioeconomic scenario. ｔ The predicted value of is stored in the prediction history storage unit 124.

[0085] The average temperature prediction unit 139 calculates the average temperature T ｔ For example, the average temperature prediction unit 139 calculates the predicted value of the regional average temperature T based on the predicted value of the greenhouse gas emissions per region calculated as the predicted value of the social index by the social index value prediction unit 133. ｔ However, the present invention is not limited to such an example.

[0086] (Impact Prediction Unit 136) As described above, the impact prediction unit 136 predicts the impact on regional per capita GDP growth for each socioeconomic scenario based on past predicted values ​​of regional subjective well-being. Information indicating the predicted impact on regional per capita GDP growth based on past predicted values ​​of regional subjective well-being is stored in the prediction history storage unit 124.

[0087] Furthermore, the impact prediction unit 136 calculates the average temperature T ｔ The impact prediction unit 136 predicts the impact on growth of per capita GDP by region for each socioeconomic scenario based on the predicted values ​​of average temperature by region. The impact prediction unit 136 stores information indicating the impact on growth of per capita GDP by region and socioeconomic scenario, which is predicted based on the predicted values ​​of average temperature by region, in the prediction history storage unit 124.

[0088] Here, the average temperature by region T ｔ The impact of this on the growth rate of per capita GDP is expressed as "δ ｔ " "δ ｔ " is the average temperature by region T ｔ This is an example of a value that indicates the impact on the growth of per capita GDP by the ｔ ” is the average temperature for year t by region, and “Tbar " is, for example, a reference temperature by region, such as the average temperature in 1995, but is not limited to such an example. δ ｔ = h(T ｔ )-h(T bar ) ... (3)

[0089] In the above formula (3), h(T) is expressed by the following formula (4). In the following formula (4), "T" is temperature, and "β1" and "β2" are coefficients. For example, "β1" and "β2" are β1 = 0.0127 and β2 = -0.0005, but are not limited to such examples. h(T) = β1 x T + β2 x T 2 ...(4)

[0090] The impact prediction unit 136 predicts the impact on the growth of per capita GDP by region for each unit period in the prediction period, where, for example, t is each unit period in the prediction period indicated in the prediction request received by the receiving unit 131. For example, if the prediction period is from 2021 to 2100, t is each one-year unit from 2021 to 2100, and the impact prediction unit 136 predicts the impact on the growth of per capita GDP by region.

[0091] (GDP prediction adjustment unit 137) The GDP prediction adjustment unit 137 adjusts the predicted value of per capita GDP by region for each socioeconomic scenario based on the predicted value of per capita GDP before adjustment calculated by the GDP prediction unit 132, the predicted value of past subjective well-being calculated by the well-being prediction unit 135, and the predicted value of average temperature calculated by the average temperature prediction unit 139.

[0092] Here, the predicted value of per capita GDP before adjustment for fiscal year t is "g t ” and the predicted value of unadjusted per capita GDP for fiscal year t-1 is “g t-1 ” and the rate of change in the adjusted forecast value of GDP per capita is “r t " " t " is the rate of change of the predicted value of unadjusted per capita GDP for fiscal year t relative to the predicted value of unadjusted per capita GDP for fiscal year t-1, and is expressed by the following formula (5): t = g t / g t-1 ...(5)

[0093] The rate of change in per capita GDP before adjustment, "r t " and "g" which is the forecast value of unadjusted GDP per capita. t " is determined depending only on the socio-economic scenario for every period t, for example, but is not limited to such an example.

[0094] Here, the predicted value of subjective happiness in the t-1 year is called "H t-1 " and the predicted value of subjective well-being in the first year, which serves as the base year, is "H0." The above formula (2) holds true from the relationship between the predicted value of subjective well-being and per capita GDP.

[0095] From the above formula (2), the following formula (6) can be derived as the rate of change in the impact of the predicted value of past subjective well-being on the growth of per capita GDP. ｔ " is the rate of change in adjusted per capita GDP, which is the rate of change in fiscal year t relative to fiscal year t-1. t =(x(t) / x(t-1))×r t ...(6)

[0096] Since "x(t)" is expressed by the above formula (1), the above formula (6) is expressed by the following formula (7). In the following formula (7), "H t-2 " is the predicted value of subjective well-being in year t-2. t = ((1 + 0.12 × (H t-1 -H0)) / (1+0.12×(H t-2 -H0)) × r t ... (7)

[0097] And the average temperature T ｔ The rate of change in per capita GDP adjusted for the effect of t ", then "r" t " is expressed by the following formula (8): t = r' t × (1 + δ t ) ... (8)

[0098] In addition, the average temperature T ｔThe growth rate of GDP per capita adjusted for the effect of t ", then "Δr" t " is expressed by the following formula (9): Δr" t = r' t ×(1+δ t ) −1 ... (9)

[0099] In addition, the adjusted forecast value of per capita GDP for fiscal year t is "g" t ", then "g" t " is the predicted value of adjusted per capita GDP for fiscal year t-1, "g" t-1 " and the growth rate for fiscal year t, "Δr" t " can be calculated from " t =Δr″ t ×g” t-1 = {r' t ×(1+δ t )}×g” t-1 ...(10)

[0100] The GDP forecast adjustment unit 137 calculates "g" which is the forecast value of the adjusted per capita GDP for fiscal year t based on the above formulas (5) to (10) and the like. t " is calculated.

[0101] [4. Processing Procedure] Next, the processing procedure of the prediction device 100 will be described. Note that the flowcharts for each embodiment will be described as a "first embodiment" and a "second embodiment." The fourth embodiment is the same as the first and second embodiments, so a description thereof will be omitted. Note that the steps described below may be executed in a different order, and some processing may be omitted. Furthermore, the processing procedures of each embodiment may be implemented in an appropriate combination.

[0102] First Embodiment First, the processing procedure of the first embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of a flowchart of a prediction method according to the first embodiment. The processing shown in Fig. 6 is processing that starts when a prediction request for a subjective well-being level is received by the receiving unit 131.

[0103] As shown in FIG. 6, the processing unit 130 of the prediction device 100 performs the following calculation:ｓｔａｒｔ -1 (step S10), and then set t=t+1 (step S11). ｓｔａｒｔ " is the start year of the prediction period indicated in the prediction request received by the receiving unit 131. For example, if the prediction period is from fiscal year 2021 to fiscal year 2100, t ｓｔａｒｔ =2021.

[0104] Next, the GDP prediction unit 132 of the processing unit 130 calculates the predicted value of per capita GDP by region in year t for each socioeconomic scenario (step S12). The social indicator value prediction unit 133 of the processing unit 130 calculates the predicted value of social indicator by region in year t for each socioeconomic scenario (step S13). The climate index value prediction unit 134 of the processing unit 130 calculates the predicted value of climate index by region in year t for each socioeconomic scenario (step S14).

[0105] The happiness prediction unit 135 of the processing unit 130 calculates a predicted value of the subjective happiness level for each region in year t based on the predicted value of per capita GDP for each region in year t after adjustment by the GDP prediction adjustment unit 137, the predicted value of the social indicator for each region in year t, and the predicted value of the climate indicator for each region in year t (step S15). ｓｔａｒｔ If so, the predicted value of per capita GDP used to calculate the predicted value of subjective well-being is the predicted value of per capita GDP that has not been adjusted in step S17.

[0106] The influence prediction unit 136 of the processing unit 130 predicts the past subjective happiness level predicted by the happiness level prediction unit 135 (for example, the above-mentioned H ｔ－１ , H ０ ), the impact on the growth of per capita GDP by region in fiscal year t is predicted for each socioeconomic scenario (step S16).

[0107] The GDP forecast adjustment unit 137 of the processing unit 130 adjusts the forecast value of per capita GDP by region in year t for each socioeconomic scenario based on the impact on the growth of per capita GDP by region in year t predicted by the impact prediction unit 136 (step S17).

[0108] When the process of step S17 is completed, the processing unit 130 determines whether t≧t ｅｎｄ It is determined whether "t ｅｎｄ " is the end year of the prediction period indicated in the prediction request received by the receiving unit 131. For example, if the prediction period is from fiscal year 2021 to fiscal year 2100, t ｅｎｄ =2100.

[0109] The processing unit 130 ｅｎｄ If it is determined that t is not t (step S18: No), the process proceeds to step S10. ｅｎｄ If it is determined that the number of the saturation points has reached the predetermined number (step S18: Yes), the process shown in FIG. 6 ends.

[0110] Second Embodiment Next, a processing procedure of a second embodiment will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of a flowchart of a prediction method according to the second embodiment.

[0111] The process shown in Fig. 7 is started when a request for predicting a subjective well-being is received by the receiving unit 131. The process of steps S20 to S25 shown in Fig. 7 is the same as the process of steps S10 to S15 shown in Fig. 6, and therefore a description thereof will be omitted.

[0112] As shown in FIG. 7, the average temperature prediction unit 139 in the processing unit 130 of the prediction device 100 calculates predicted values ​​of average temperatures by region in year t for each socioeconomic scenario (step S26).

[0113] Next, the influence prediction unit 136 of the processing unit 130 predicts the predicted value of the past subjective happiness calculated by the happiness prediction unit 135 (for example, the above-mentioned H ｔ－１ , H ０ ) and the predicted value of the average temperature by region in year t calculated by the average temperature prediction unit 139, the impact on the growth of per capita GDP by region in year t is predicted for each socioeconomic scenario (step S27).

[0114] Next, the GDP forecast adjustment unit 137 of the processing unit 130 adjusts the forecast value of per capita GDP by region in year t for each socioeconomic scenario based on the impact on the growth of per capita GDP by region in year t predicted by the impact prediction unit 136 (step S28).

[0115] In step S27, the impact prediction unit 136 separately predicts the impact on per capita GDP growth based on the predicted value of past subjective well-being and the impact on per capita GDP growth based on the predicted value of average temperature, but it can also predict the impact on per capita GDP growth based on the predicted value of past subjective well-being and the predicted value of average temperature.

[0116] 5. Other Embodiments In the above-described example, the processing unit 130 calculates various predicted values ​​on an annual basis, but the present invention is not limited to such an example, and various predicted values ​​can also be calculated on a prediction unit other than an annual unit (for example, on a monthly or weekly basis). For example, when a prediction request includes information indicating the prediction unit, the processing unit 130 predicts various predicted values ​​on the prediction unit specified in the prediction request.

[0117] In the above example, the processing unit 130 calculates the predicted value of subjective well-being based on the predicted value of adjusted per capita GDP, the predicted value of social indicators, and the predicted value of climate, but this is not a limitation. For example, the processing unit 130 can calculate the predicted value of subjective well-being based on the predicted value of adjusted per capita GDP without using the predicted values ​​of social indicators and climate. The processing unit 130 can also calculate the predicted value of subjective well-being based on one of the predicted values ​​of social indicators and the predicted value of climate and the predicted value of adjusted per capita GDP.

[0118] Furthermore, the processing unit 130 may be configured without the GDP prediction unit 132, the social index value prediction unit 133, the climate index value prediction unit 134, and the average temperature prediction unit 139. In this case, the receiving unit 131 can receive predicted values ​​of per capita GDP by region, predicted values ​​of social indexes by region, predicted values ​​of climate indexes by region, and average temperatures by region for each socioeconomic scenario from an external device via the communication unit 110.

[0119] In this case, the processing unit 130 calculates the predicted value of the subjective well-being index for each region for each socioeconomic scenario based on the predicted value of per capita GDP for each region, the predicted value of social indicators for each region, the predicted value of climate indicators for each region, and the predicted value of average temperature for each region received by the receiving unit 131.

[0120] Furthermore, the predicted value of per capita GDP after adjustment by the GDP prediction adjustment unit 137 is used by the social index value prediction unit 133 and the happiness prediction unit 135, as described above, but is not limited to this example and may be used, for example, by either the social index value prediction unit 133 or the happiness prediction unit 135.

[0121] 6. Effects The prediction device 100 according to this embodiment includes an impact prediction unit 136, a GDP prediction adjustment unit 137, and a well-being prediction unit 135. The impact prediction unit 136 predicts the impact on per capita GDP growth based on the predicted value of past subjective well-being. The GDP prediction adjustment unit 137 adjusts the predicted value of per capita GDP based on the impact predicted by the impact prediction unit 136. The well-being prediction unit 135 calculates a predicted value of subjective well-being based on the predicted value of per capita GDP adjusted by the GDP prediction adjustment unit 137. This allows the prediction device 100 to use a predicted value of per capita GDP after adjustment that takes into account the impact of subjective well-being, thereby improving the accuracy of the predicted value of subjective well-being. This allows subjective well-being to be more appropriately used as an indicator when considering the formulation of measures and policies.

[0122] The prediction device 100 also includes an average temperature prediction unit 139 that calculates a predicted value of the average temperature, and the impact prediction unit 136 predicts the impact on per capita GDP growth based on the predicted value of past subjective well-being and the predicted value of the average temperature calculated by the average temperature prediction unit 139. This allows the prediction device 100 to use a predicted value of per capita GDP after adjustment that takes into account the effects of subjective well-being and average temperature, and improves the accuracy of the predicted value of subjective well-being, thereby making it possible to more appropriately use subjective well-being as an indicator when considering the formulation of measures and policies.

[0123] [7. Hardware Configuration] The components of each device shown in the figures are conceptual functional components and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown, and all or part of the devices can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0124] Furthermore, among the processes described in this embodiment, all or part of the processes described as being performed automatically can also be performed manually using known methods. In addition, the information including the processing procedures, control procedures, specific names, various data, and parameters shown in the drawings can be changed as desired unless otherwise specified.

[0125] [Program] In one embodiment, the various devices constituting the prediction device 100 can be implemented by installing the above-described prediction program as package software or online software on a desired computer. For example, by executing the above-described prediction program on an information processing device, the various devices constituting the prediction device 100 can function. The information processing device referred to here includes desktop and notebook personal computers. In addition, the information processing device also includes mobile communication terminals such as smartphones and mobile phones, and even slate terminals such as PDAs (Personal Digital Assistants).

[0126] 8 is a diagram showing an example of a computer in which the prediction device 100 according to this embodiment is realized. The computer 1000 includes, for example, a memory 1010 and a CPU 1020. The computer 1000 also includes a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0127] The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0128] The hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, a program that defines each process of the various devices that constitute the prediction device 100 is implemented as a program module 1093 in which computer-executable code is written. The program module 1093 is stored, for example, in the hard disk drive 1090. For example, a program module 1093 for executing processes similar to those of the functional configurations of the various devices that constitute the prediction device 100 is stored in the hard disk drive 1090. Note that the hard disk drive 1090 may be replaced by an SSD (Solid State Drive).

[0129] Furthermore, setting data used in the processing of the above-described embodiment is stored as program data 1094, for example, in the memory 1010 or the hard disk drive 1090. The CPU 1020 then reads the program module 1093 or the program data 1094 stored in the memory 1010 or the hard disk drive 1090 into the RAM 1012 as necessary, and executes the processing of the above-described embodiment.

[0130] The program module 1093 and program data 1094 are not limited to being stored in the hard disk drive 1090, but may also be stored in, for example, a removable storage medium and read by the CPU 1020 via the disk drive 1100. Alternatively, the program module 1093 and program data 1094 may be stored in another computer connected via a network (such as a LAN or a WAN (Wide Area Network)). The program module 1093 and program data 1094 may then be read by the CPU 1020 from the other computer via the network interface 1070.

[0131] [8. Other] While the present embodiment has been described above, the present embodiment is not limited by the descriptions and drawings that form part of the disclosure. In other words, other embodiments, examples, and operational techniques made by those skilled in the art based on the present embodiment are all included in the scope of the present embodiment.

[0132] 100 Prediction device 110 Communication unit 120 Memory unit 121 GDP actual value memory unit 122 Social index actual value memory unit 123 Climate actual value memory unit 124 Prediction history memory unit 125 Socioeconomic scenario information memory unit 130 Processing unit 131 Reception unit 132 GDP prediction unit 133 Social index value prediction unit 134 Climate index value prediction unit 135 Happiness prediction unit 136 Impact prediction unit 137 GDP prediction adjustment unit 138 Provision unit 139 Average temperature prediction unit

Claims

1. A prediction device comprising: an impact prediction unit that predicts the impact on per capita GDP growth based on predicted values ​​of past subjective well-being; a GDP prediction adjustment unit that adjusts the predicted value of per capita GDP based on the impact predicted by the impact prediction unit; and a happiness prediction unit that calculates a predicted value of subjective well-being based on the predicted value of per capita GDP adjusted by the GDP prediction adjustment unit.

2. The prediction device according to claim 1, further comprising an average temperature prediction unit that calculates a predicted value of the average temperature, wherein the impact prediction unit predicts the impact on the growth of the per capita GDP based on the predicted value of the past subjective well-being and the predicted value of the average temperature.

3. A prediction method executed by a computer, comprising: an impact prediction step of predicting the impact on per capita GDP growth based on predicted values ​​of past subjective well-being; a GDP prediction adjustment step of adjusting the predicted value of per capita GDP based on the impact predicted by the impact prediction step; and a happiness prediction step of calculating a predicted value of subjective well-being based on the predicted value of per capita GDP adjusted by the GDP prediction adjustment step.

4. A prediction program that causes a computer to execute the following steps: an impact prediction step that predicts the impact on per capita GDP growth based on predicted values ​​of past subjective well-being; a GDP prediction adjustment step that adjusts the predicted value of per capita GDP based on the impact predicted by the impact prediction step; and a happiness prediction step that calculates a predicted value of subjective well-being based on the predicted value of per capita GDP adjusted by the GDP prediction adjustment step.

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