This modeling unit uses system dynamics approaches to simulate cross-sectoral energy systems (energy, land and sea transport, agriculture, industry, etc.), their GHG emissions and decarbonization potential, including technological and economic solutions.  The Climate Hub Unit focusing on Climate and Energy Systems modeling will use system dynamics, linear and mixed integer optimization and stochastic modelling techniques to develop decarbonization pathways of the energy system at the national level.

1. Detailed inventories of the:

• energy supply will be conducted by mapping power generation plants along with their associated fuel, including coal, oil, gas, renewables, bioenergy, nuclear and new zero carbon.
• energy demand and use of different sectors such as transport, households, buildings and industry will be recorded along with their associated greenhouse gas (GHG) emissions.
• Various climate solutions, such as carbon pricing will be tested and their effect on GHG emissions and overall temperature will be calculated. Scenario building will include the implementation of different fuels for power generation, electrification schemes for the transport or building sector, along with different energy efficiencies and the result will be a simulation of the scenario providing detailed values for all relevant variables, along with the resulting temperature increase.

Models:

• Balmorel Energy-System model from collaborating institute DTU.
• LEAP from collaborating with ASEAN Green Future project.
• En-ROADS climate simulation model from collaborating with MIT Climate Interactive.

2. Develop tailor-made models for national and/or regional -scale – focusing on specific needs or characteristics of the region under consideration. The models will focus on spatiotemporal aspects as well as model uncertainty aspects and will use a holistic approach blending techniques from system dynamics with scientific computing, statistical modelling and machine learning tools.

3. The input of natural capital as well as climate effects on it will be an important element of our analysis. We intend to co-evolve the models along with the currently under construction Ecosystems Valuation Database gradually leading to a tool that may connect past studies through elaborate meta-analysis to reliable estimates and predictions for regions under considerations and their dependence on various climate scenarios. The data base will also feed into the tailor-made models into the modules requiring quantification of natural capital. This will be a crucial link to policy, by prioritizing actions for areas and ecosystem services that are degraded.

Results will be calibrated and tested against the suite of large Integrated Assessment Models (IAMs) from prestigious institutes around the world, such as IIASA (GAINS,GLOBIOM-G4M) and PNNL (ESM).