Knowledge Based Climate Mitigation Systems for a Low Carbon Economy (COMPLEX)

Overview

Current models of climate change and carbon emission assume that the immediate past is a reasonable guide to the future. They struggle to represent the complex causal structures and time-asymmetries of many socio-natural systems. COMPLEX integrates a suite of different climate-economic models operating at different scales (global, regional and individual) with our best understanding of fine-grained space-time patterns and the system-flips that are likely to occur in the long interval between now and 2050. Our task is to help policy makers facilitate the change to low-carbon economy.                

COMPLEX is an EU funded project that has brought an international team of 17 partners across 11 European countries together to explore new energy technologies, new ways of using landscapes and new policy instruments to support the transition towards a low carbon society.

IIASA Research

The goal of IIASA work is to build and analyze the hierarchy of models, which were developed within the COMPLEX project. ASA researchers explore the different models to understand how information from more aggregated qualitative models can be transmitted to more elaborated and detailed quantitative simulations, and vice versa.

A new Bayesian methodology is developed to obtain integrated knowledge from alternative sources that view the same system from various perspectives. The goal of the ASA research is to expand this methodology to dynamical models with random components. Based on a dynamic version of the Bayesian integration methodology, the climate-energy-economic system can be represented on a higher level by integrated models, which capture features of each of the originally given models and filter mutually incompatible local transitions between them.

ASA researchers study data-driven stochastic qualitative models for structural sensitivity to see what happens to system behavior observed in some simpler models, when more details are added to models and when some of these features become no longer evident in the model performance. The target is to estimate the probabilities of system flips in the future under the business-as-usual scenario. Growth in the value of the estimated probability for a flip to occur will give a warning signal. The methodology is intended to be applied in analysis of future policy scenarios, alternative to the business-as-usual one.

Last edited: 23 October 2017