| Project/Area Number |
22K18039
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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 63040:Environmental impact assessment-related
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| Research Institution | Kyushu University |
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Principal Investigator |
Chapman Andrew 九州大学, カーボンニュートラル・エネルギー国際研究所, 准教授 (60795293)
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
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| Keywords | energy system / preference / behavior / machine learning / artificial intelligence / social equity / system design / environmental behavior / energy system design / statistical analysis / survey / Social Equity / Energy System / Behavior / Sustainability |
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| Outline of Research at the Start |
This research utilizes big data, national surveys, consumption (behavioral) data and direct stakeholder input to establish a machine learning model and visualization interface to detail stakeholder's social aspects and preferences toward an equitable and desirable future energy system.
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| Outline of Final Research Achievements |
This research seeks to understand how people’s daily behaviors and preferences may influence their perceived importance of environmental, economic and social issues. To date a lot of research has been grounded in survey and statistical analysis-based approaches. Here, we seek determine the efficacy of decision tree machine learning approaches which only employ non-identifiable data to estimate people’s perceived issue importance based predominantly on behavioral inputs. Machine learning approaches as proposed in our framework can make predictions as to whether certain issues are important to people based not only on demographics but also on a suite of daily behaviors. This framework may provide a streamlined policy instrument for policymakers to develop energy policies which align with people’s values and therefore may be more effective for energy system design. In this research project we submitted two journal articles, 1 published and 1 is under review.
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| Academic Significance and Societal Importance of the Research Achievements |
The research is scientifically significant as it allows us to streamline the acquisition of data and it's application to machine learning to identify factors and preferences that were either unclear, or unable to be extracted from small data sets. Energy system design applications are also exciting.
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