Artem Baklanov
Research Scholar
Novel Data Ecosystems for Sustainability Research Group
Advancing Systems Analysis
Biography
Artem Baklanov joined IIASA as a Postdoctoral Research Scholar in September 2014. He is currently affiliated with the Exploratory Modeling of Human-Natural Systems (EM) research group within the Advancing Systems Analysis (ASA) program, where he works on the application of control theory, game theory, and machine learning to inform environmental decision making. Currently, he develops tools for a trade-off analysis in Integrated Assessment Models (IAMs) and studies consistency between short-term policies and long-term targets. Dr. Baklanov applies the attainable set approach to circumscribe possible short-term actions that are consistent with a specified long-term target, as well as to reveal which long-term targets are still attainable depending on a chosen short-term policy.Last update: 22 JAN 2021
Publications
Bednar, J., Baklanov, A. , & Macinante, J. (2023). The Carbon Removal Obligation: Updated analytical model and scenario analysis. IIASA Working Paper. Laxenburg, Austria: WP-23-001
Bednar, J., Obersteiner, M. , Baklanov, A. , Thomson, M., Wagner, F. , Geden, O., Allen, M., & Hall, J.W. (2021). Operationalizing the net-negative carbon economy. Nature 596 377-383. 10.1038/s41586-021-03723-9.
Baklanov, A. (2021). Reactive Strategies: An Inch of Memory, a Mile of Equilibria. Games 12 (2) e42. 10.3390/g12020042.
Rovenskaya, E. , Aghababaei Samani, K., Baklanov, A. , Ermolieva, T., Folberth, C. , Fritz, S. , Hadi, H., Javalera Rincón, V. , Krasovskii, A. , Laurien, F. , Poblete Cazenave, M., Schinko, T. , Smilovic, M. , & Zebrowski, P. (2019). Artificial Intelligence and Machine Learning for Systems Analysis of the 21st Century. IIASA Working Paper. Laxenburg, Austria: WP-19-010
Schepaschenko, D. , See, L. , Lesiv, M. , Bastin, J.-F., Mollicone, D., Tsendbazar, N.-E., Bastin, L., McCallum, I. , Laso Bayas, J.C. , Baklanov, A. , Perger, Ch., Dürauer, M., & Fritz, S. (2019). Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery. Surveys in Geophysics 40 (4) 839-862. 10.1007/s10712-019-09533-z.