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Our research projects

The research topics of Matsumoto Lab. are broad in environmental, energy, and resource issues and related policies. Our special interests and specialized fields are climate change and energy fields with quantitative methods such as computable general equilibrium (CGE) models, statistical and econometric models, and various indicators. We implement a lot of collaborative work with not only domestic researchers but also international collaborators.

The details of my information can be found on the Member page. If you are interested in collaborating with us for working/studying at our laboratory, please contact us.

Keywords: Environmental and energy economics and policy; Climate change; Sustainable development; Market; Scenario; CGE; Statistics; Sconometric models; Agent-based model; Machine learning; Indicators; Policy analysis

Funds: Grant-in-Aid for Scientific Research (B)×5, (C)×1, International collaboration (b)×1, MEXT Advanced Research Program for Climate Change Prediction, Environment Research and Technology Development Fund, Asahi Glass Foundation

The current research topics are as follows. If you are interested in the related topics, please contact us.
Quantitative analysis
  • Analysis of climate impact, mitigation, and adaptation using CGE and integrated assessment models
  • Evaluation of energy security using economic models
  • Air pollution, health and economic impacts
  • Forest development, agriculture, and poverty in Indonesia
  • Decomposition of CO2 and air pollutant emissions on national and regional scales
  • Environmental performance with DEA models
  • Economic analysis of electricity markets
  • Comparison of environmental policies with multi-stage evaluation methods 
  • Decisionmaking of farmers​
  • Renewable energy in remote islands
Qualitative analysis
  • Climate change mitigation, impact, and adaptation, and ecosystem conservation
  • Science-policy interface to achieve SDGs
  • ​Nature-based Solutions and governance


Matsumoto et al. (2021) ERC


Tembata et al. (2020) STOTEN

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