International Institute for Applied Systems Analysis

About: International Institute for Applied Systems Analysis is a nonprofit organization based out in Laxenburg, Austria. It is known for research contribution in the topics: Population & Greenhouse gas. The organization has 1369 authors who have published 5075 publications receiving 280467 citations. The organization is also known as: IIASA.

Global energy-climate scenarios and models: A review

Abstract: Long-term energy scenarios are an important input to policy-relevant assessment reports on climate change such as those produced by the Intergovernmental Panel on Climate Change or the United Nations Environment Programme to just name a few examples. They are also used by government agencies to support their decision making in the context of climate change mitigation and other energy-related challenges. In response to this demand, two broader categories of model development are currently pursued by the scientific community: (1) the degree of integration is increasing, in other words, the system boundaries of models are being extended, in particular to address the interlinkages between the energy, land, food, water, and climate more comprehensively and (2) the heterogeneity of the representation of various entities (e.g., spatial, sectoral, socio-economic) is increasing to adequately address distributional effects (e.g., countries within regions, urban vs rural areas, different types of households). Moreover, the energy-climate scenarios that are being developed are designed to be more .realistic. by going beyond very stylized designs and integrate features that are observed in the real world. This includes delayed action on climate mitigation or fragmented approaches to mitigation that not only exclude major emitters from climate action, but also the exclusion of specific technologies from the portfolio of mitigation options in response to technical challenges or public acceptance issues. Finally, an attempt is made to summarize robust insights that have emerged from individual studies and particularly from modeling comparison exercises.

Using Nonlinear Model Predictive Control for Dynamic Decision Problems in Economics

Abstract: This paper presents a new approach to solve dynamic decision models in economics. The proposed procedure, called Nonlinear Model Predictive Control (NMPC), relies on the iterative solution of optimal control problems on finite time horizons and is well established in engineering applications for stabilization and tracking problems. Only quite recently, extensions to more general optimal control problems including those appearing in economic applications have been investigated. Like Dynamic Programming (DP), NMPC does not rely on linearization techniques but uses the full nonlinear model and in this sense provides a global solution to the problem. However, unlike DP, NMPC only computes one optimal trajectory at a time, thus avoids to grid the state space and for this reason the computational demand grows much more moderate than for DP. In this paper we explain the basic idea of NMPC together with some implementational details and illustrate its ability to solve dynamic decision problems in economics by means of numerical simulations for various examples, including stochastic problems, models with multiple equilibria and regime switches in the dynamics.

The generation of gridded emissions data for CMIP6

Abstract: . Spatially distributed anthropogenic and open burning emissions are fundamental data needed by Earth system models. We describe the methods used for generating gridded datasets produced for use by the modeling community, particularly for the Coupled Model Intercomparison Project Phase 6. The development of three sets of gridded data for historical open burning, historical anthropogenic, and future scenarios was coordinated to produce consistent data over 1750–2100. Historical data up to 2014 were provided with annual resolution and future scenario data in 10-year intervals. Emissions are provided on a sectoral basis, along with additional files for speciated non-methane volatile organic compounds (NMVOCs). An automated framework was developed to produce these datasets to ensure that they are reproducible and facilitate future improvements. We discuss the methodologies used to produce these data along with limitations and potential for future work.