A ground-breaking study into sustainable solutions to jointly meet water, energy and land demands at the global level, and also zooming into two large transboundary basins facing multiple development and environmental challenges: The Zambezi and the Indus. More
The IIASA futures initiatives are cross-sectoral projects designed to explore plausible futures for a number of the world’s rapidly transitioning regions or resources. Current futures initiatives cover a spectrum of issues encompassing the Arctic, tropical regions, economic integration in Eurasia, and water management around the world. More
Cross-cutting research at IIASA is implemented through a number of internally funded projects. These primarily methodology-focused projects represent unique and unaddressed research challenges that require integrated and interdisciplinary expertise and focus. These projects address not only cutting edge research questions, but, by drawing upon expertise from across the IIASA research programs, also promote greater collaboration and integration across the institute. More
The Systems Analysis Forum (SAF) facilitates and catalyzes methodological research at IIASA. These small-scale projects promote methodological advances that push the envelope of scientific excellence in systems analysis at an international level, introduce new approaches broadening the institute’s systems-analysis capabilities, and/or innovatively demonstrate the applicability of promising methods to new targets. More
Last edited: 24 July 2019
Models and Tools
Nielson SN, Fath B ORCID: https://orcid.org/0000-0001-9440-6842, Bastianoni S, Marques JC, Müller F, Patten BC, Ulanowicz RE, Jørgensen SE, et al. (2020). A New Ecology: Systems Perspective, Second Edition. Elsevier. ISBN 978-0-444-63757-4 DOI:10.1016/C2015-0-01948-7.
Wang M, Tang T ORCID: https://orcid.org/0000-0002-2867-9241, Burek P ORCID: https://orcid.org/0000-0001-6390-8487, Havlik P, Krisztin T, Kroeze C, Leclere D, Strokal M, et al. (2019). Increasing nitrogen export to sea: A scenario analysis for the Indus River. Science of the Total Environment 694: e133629. DOI:10.1016/j.scitotenv.2019.133629.
Barbosa LF, Nascimento A, Mathias MH, & de Carvalho JA (2019). Machine learning methods applied to drilling rate of penetration prediction and optimization - A review. Journal of Petroleum Science and Engineering 183: e106332. DOI:10.1016/j.petrol.2019.106332.
International Institute for Applied Systems Analysis (IIASA)
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Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313