This study proposes an approach which permits assessing net emission changes under the Kyoto Protocol, in particular with respect to atmospheric CO2 and CO2 emissions from fossil fuel burning, cement manufacture and gas flaring; whereby emission changes are characterized by uncertainty distributions in terms of verification times (VTs). More
Stochastic Quasi-Gradient (SQG) methods have been developed for solving general optimization problems without exact calculation of objective function and constraints (let alone of their derivatives). SQG methods enable a sequential revision of approximate solutions towards the optimal using newly acquired information on the system, obtained via either direct on-line observations or(and) simulations. More
The linkage algorithms solve the problem of linking models, e.g. sectorial and/or regional, into an inter-sectorial inter-regional integrated model. Linkage enables to avoid “hard linking” of models in a single code, which saves the programming time and enables parallel distributed computations of individual models instead of a large scale integrated model. Models linkage preserves the structure of the original models taking into account critically important details, which are usually missing in aggregate models More
Last edited: 07 April 2021
Research Group Leader and Principal Research Scholar Integrated Biosphere Futures Research Group - Biodiversity and Natural Resources Program
Stark, S., Biber-Freudenberger, L., Dietz, T., Escobar Lanzuela, N. , Förster, J.J., Henderson, J., Laibach, N., & Börner, J. (2022). Sustainability implications of transformation pathways for the bioeconomy. Sustainable Production and Consumption 29, 215-225. 10.1016/j.spc.2021.10.011.
Hlásny, T., Augustynczik, A., & Dobor, L. (2021). Time matters: Resilience of a post-disturbance forest landscape. Science of the Total Environment 799, e149377. 10.1016/j.scitotenv.2021.149377.
Kuschnig, N., Crespo Cuaresma, J., Krisztin, T. , & Giljum, S. (2021). Spatial spillover effects from agriculture drive deforestation in Mato Grosso, Brazil. Scientific Reports 11 (1) 10.1038/s41598-021-00861-y.
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