The National Research Foundation of Korea (NRF) is the National Member Organization (NMO) representing Korean membership of IIASA as well as funding IIASA’s annual membership fee.
Main areas of collaborations:
Identifying measures to reduce air pollutants and greenhouse gas emissions
Research to support green growth in Korea
Improving the early warning of forest fires and the ecosystem value of forests in Korea
Studying the vulnerabilities and sources of resilience of the Korean economy and society
Advancing energy and integrated assessment modeling in Korea
Analyzing global water challenges
Are you a member of our Korean regional community? IIASA Connect is our exclusive platform bringing this network together. Join today!
25 July 2023
26 June 2023
29 June 2023 Seoul, Korea
12 October 2022 Seoul, Republic of Korea and online
04 December 2022
07 November 2022
29 November 2021
Heo, N., Chang, H.-C., & Abel, G. (2023). Investigating the distribution of university alumni populations within South Korea and Taiwan based on data from the LinkedIn advertising platform. Cities 137 e104315. 10.1016/j.cities.2023.104315. Jo, H.-W., Krasovskiy, A. , Hong, M., Corning, S., Kim, W., Kraxner, F., & Lee, W.-K. (2023). Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach. Remote Sensing 15 (5) e1446. 10.3390/rs15051446. Chong, H., Lee, S., Cho, Y., Kim, J., Koo, J.-H., Pyo Kim, Y., Kim, Y. , Woo, J.-H., & Hyun Ahn, D. (2023). Assessment of air quality in North Korea from satellite observations. Environment International 171 e107708. 10.1016/j.envint.2022.107708. Kumar, N., Johnson, J., Yarwood, G., Woo, J.-H., Kim, Y. , Park, R.J., Jeong, J.I., Kang, S., Chun, S., & Knipping, E. (2022). Contributions of domestic sources to PM2.5 in South Korea. Atmospheric Environment 287 e119273. 10.1016/j.atmosenv.2022.119273. Jo, H.-W. (2022). Optimization of the IIASA’s FLAM model to represent forest fires in South Korea. IIASA YSSP Report. Laxenburg, Austria: IIASA Cha, S., Jo, H.-W., Kim, M., Song, C., Lee, H., Park, E., Lim, J., Shchepashchenko, D. , Shvidenko, A., & Lee, W.-K. (2022). Application of deep learning algorithm for estimating stand volume in South Korea. Journal of Applied Remote Sensing 16 (02) e024503. 10.1117/1.JRS.16.024503.