| Project/Area Number |
23K12463
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 07040:Economic policy-related
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| Research Institution | Hitotsubashi University |
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Principal Investigator |
Wang Hongming 一橋大学, 社会科学高等研究院, 非常勤研究員 (20867048)
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| Project Period (FY) |
2023-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2024: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2023: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | climate change / mortality / machine learning / health |
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| Outline of Research at the Start |
The proposed research aims to quantify the mortality consequences of climate change using daily weather variations over a 50-year period in Japan. To account for the complex and non-linear effects of climate on health, the statistical analysis will use machine learning methods such as neural networks to quantify the dynamic climate-mortality relationship based on historic data. Building on the relationship, the research will project future mortality effects under different scenarios of climate change and discuss implications for mitigation policies in high-mortality areas.
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| Outline of Annual Research Achievements |
The purpose of the research is to understand the impacts of climate change on mortality in Japan and to provide insights for policies mitigating the health impacts of climate change. So far, I have collected satellite re-analysis data and have processed the data to derive daily weather variations in temperature, humidity, precipitation, and wind speed across municipalities in Japan. Based on the weather data, I quantified the magnitude of climate change across Japan focusing on changes in the frequency and intensity of extreme weather events relative to their local climatology in 1951-1980. I show that over time, abnormally hot days with maximum temperature above the 90th percentile of local climatology increased in frequency across Japan, and the intensity of extreme heat above the historic normal has risen sharply in recent decades.
In addition to changes in univariate extremes in temperature and precipitation, climate change is also characterized by increases in compound extreme events such as prolonged heatwaves, hot-and-drought periods, and days with both high temperature and humidity. In light of their potentially devastating impacts on natural systems and human health, I quantify the magnitude of compound extreme events in Japan using respective indices developed in the climate literature. Building on the climate variables, in year 2024, I will explore the heterogeneous and non-linear relationship between mortality and climate drivers using machine learning.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
The research is slightly delayed relative to schedule for two reasons. First, I attempted to obtain individual-level mortality data covering the period 1972-2019 from the Ministry of Health, Labour and Welfare of Japan. I was not able to get access to the data from the Ministry. I later proceeded with using publicly available municipality-level mortality data covering the period 1980-2019 for my research. For this reason, the estimation sample covers the period 1980-2019 instead of the 1972-2019 period as initially outlined in the proposal. Second, there was an interruption in the computing service available in my office. I initially conducted exploratory data analysis using the Lenovo desktop housed in my office, but in early 2024, the desktop was moved to a separate office to be used exclusively by a different research team. I then purchased the Dell Precision 7865 desktop to continue my own work. However, I was not able to purchase additional memory capacity for the desktop using the FY2023 budget. The memory upgrade will be completed using budget from FY2024.
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| Strategy for Future Research Activity |
Building on the climate variables quantified in year 2023, in year 2024, I employ machine learning to empirically characterize the relationship between mortality and climate change and to identify high-risk areas most vulnerable to the health impacts of climate change. Compared to linear regression models, machine learning has the advantage that it can non-parametrically quantify the mortality responses to a variety of climate drivers without imposing strong functional form assumptions such as linearity.
I apply several machine learning methods including penalized regressions, decision trees, and neural network, and use cross validation to select the best-performing model to reveal the non-linear relationship between mortality and climate drivers. To track the impacts of climate change over time, and to detect any meaningful adaptation that might mitigate the mortality consequences in more recent periods of climate change, I quantify the mortality response curves separately for 1980-1999 and 2000-2019. A flattened response curve to extreme heat in 2000-2019 is consistent with adaptation to the climate hazard in recent history. Across climate factors, I use the model to identify the leading hazards contributing to mortality, and across locations, I identify areas most vulnerable to climate-induced mortality due to increased exposure to such hazards. Due to difficulties in accessing individual mortality records that cover the period 1972-2019, I will focus on the period 1980-2019 where municipality-level mortality data is publicly available.
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