In the today’s world, interdependent food-energy-water-environment (FEWE) security goals contribute immensely to signifying the nexus between corresponding sectors, notably via overall economic and environmental goals, including quotas on total water pollution, GHG emissions, technology investments etc. The current practice of developing and using separate models of separate sectors and regions forces researchers to make additional assumptions on how the common resources utilized by the considered sectors and regions are to be divided.
The sectorial and regional models identify solutions optimal for the considered sectors and regions. When competition for resources becomes binding, an independent analysis of sectors and regions ignoring their interconnectedness can become highly misleading. Hence the sectorial and regional models must be linked together to produce truly integrated solutions optimal for the overall economy, in which they are a part.
Linkage algorithms solve the problem of linking sectorial and/or regional models into an inter-sectorial inter-regional integrated model. The approaches for linking models are based on iterative procedures of nondifferentiable and stochastic optimization using generalized gradients and stochastic quasi gradients methods that do not require models to exchange full information about their specifications. Models linkage enables to avoid “hard linking” of the 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. Therefore, models linkage preserves the structure of the original models taking into account critically important details, which are usually missing in aggregate models.
Linkage algorithms can be implemented on distributed computers, representing individual sectorial or regional models. Capacity of the computer network enables application of these algorithms to large-scale models to support real-time decision making processes, for example, regarding robust trades in emission/pollution trading markets.
On-going research activities
Models linkage for robust energy-water-food NEXUS management
ASA, ESM, ENE and WAT programs develop new SQG-based approaches for iterative linkage of distributed agricultural, (bio)energy, water, natural disasters models to derive robust integrative solutions across sectors and regions under asymmetric information, uncertainty, systemic (dependent) risks.
Linkage of simulation and optimization models
IIASA cross-program initiative between ASA, WAT, and ESM programs develops novel methods combining simulation and stochastic optimization for optimal and robust water-food-energy-environmental NEXUS management under uncertainty and resource scarcity.
Multi-agent emission and pollution trading systems (markets):
ASA, ESM and AIR programs jointly develop stochastic emission and pollution abatement and trading models, which integrate emissions/pollution reduction technologies and various costs, allowing to analyze the robustness of pollution and uncertainty reduction policies under environmental safety constraints, asymmetric information, inherent uncertainties and risks. The models are comprised of individual parties’ (sectors, regions, agents) models and a social planner model. For linking parties’s and social planner models, specific methodology of decentralized (distributed) optimization is applied. The trading models can be viewed as a prototype to simulate an emission trading market that is regulated in a decentralized way.
Last edited: 15 January 2018
Ermoliev Y, Ermolieva T, Jonas M , Obersteiner M , Wagner F , & Winiwarter W (2015). Integrated model for robust emission trading under uncertainties: cost-effectiveness and environmental safety. Technological Forecasting and Social Change 98: 234-244. DOI:10.1016/j.techfore.2015.01.003.
Ermolieva T, Ermoliev Y, Jonas M , Obersteiner M , Wagner F , & Winiwarter W (2015). Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach. In: Uncertainties in Greenhouse Gas Inventories. Expanding Our Perspectives. Eds. Ometto, J.P., Bun, R., Jonas, M. & Nahorski, Z., pp. 183-196 Cham, Switzerland: Springer International. ISBN 98-3-319-15900-310.1007/978-3-319-15901-0_13.
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