| Project/Area Number |
20K20027
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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 64060:Environmental policy and social systems-related
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| Research Institution | Okinawa Institute of Science and Technology Graduate University |
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Principal Investigator |
Shah Payal 沖縄科学技術大学院大学, サイエンステクノロジーグループ, サイエンス・テクノロジーアソシエイト (30773220)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2021: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | modern portfolio theory / climate change / conservation policy / real options / conservation planning / diversiifcation / portfolio optimization / uncertainty / diversification / conservation / dynamic optimization |
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| Outline of Research at the Start |
This research will be carried out in 2 steps:
1)I will develop a decision framework for environmental protection under climate change by integrating 2 risk-reduction strategies: a) portfolio theory to diversify across environmental projects at a point in time, and b) real options analyses to determine dynamic modification of environmental actions over time
2) I will apply the new decision framework to coastal conservation planning:
a) Collect data on coastal wetland changes under different sea level rise scenarios from NOAA
b) Apply new decision framework from step 1 to data collected in step a
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| Outline of Final Research Achievements |
Modern portfolio theory is used to guide conservation efforts aimed at reducing climate chnage driven uncertainty in future conservation outcomes. However, standard portfolio theory has two main shortcomings: 1) it is information intensive and cannot be applied for fine-sclae conservation planning; and 2) it is static in nature and fails to accountfor temporal changes associated with climate uncertainty. We modify the portfolio optimization probelm to address these problems. We identify and compare three robust statistical estimators that can be used for systematic conservation planning under climate change with insufficient information. We also combine the standard portfolio theory framework with Marxan to minimize the risk of climate uncertainty for fine scale conservation planning. Finally, we combine protfolio theory with real options analyses to develop a framework that can consider both spatial and temporal uncertainties associated with climate change in conservation settings.
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| Academic Significance and Societal Importance of the Research Achievements |
Climate change poses dire threats to species, biodiversity and ecosystem services and creates challenges for conservation planning. The methods developed in this project can help conservation planners design effective policy that can efficiently manage the risk from climate uncertainty.
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