Addressing Uncertainty in TIMES Using Monte Carlo Methods

This project extended the TIMES modelling framework with a computationally efficient Monte Carlo Analysis (MCA) method suitable for probabilistic risk assessment. By attaching probabilities to inputs, MCA enables the quantification of uncertainty, such as the likelihood of achieving specific policy targets and the relationships between inputs and outputs. Correlated inputs can be represented by joint probability distributions, which are then passed into the MCA. The methodology was tested with the ETSAP-TIAM model to analyse 2°C pathways.

The report consists of two parts: i) The technical documentation with the implementation details (the source codes needed to include the method is also given). ii) The scientific report regarding the probabilistic assessment with ETSAP-TIAM