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.

En-ROADS climate simulation model from collaborating with MIT Climate Interactive.

Data sources:

 include the International Energy Agency (IEA) and IPCC.

2. Develop statistical and machine learning approaches to downscaling global models to national, regional or sectoral level. The methodology will use input from large scale models and create statistical and machine learning models that connect the large-scale results to the required quantities of interest at the local level. By required quantities we mean, either environmental or socioeconomic variables. We also use techniques from model averaging.

Potential models used (indicative list):

BayesPop: for population probabilistic projections

MaGE: key economic indicators projections

CAPRI: Common Agricultural Policy Regional Impact

REMIND: Regional Model of Investment and Development

WITCH Model: Economic planning based on emissions, dependence on exhaustible natural resources, etc.

3. 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.

4. 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.

• 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).

5. Different Shared Socioeconomic Pathways (SSPs) will be provided against different RCPs to provide a matrix of options, mapping the different pathways that a nation might follow and evolve considering climate policy scenarios and challenges. The scenarios will not be rigid, but the user will be allowed to increase or decrease specific variables and see the effects across a range of quantities. Analysis will result in specific outcomes, fully quantified and visualized, along with corresponding effects on emissions and temperature increase.