========================= How to use the models? ========================= We used a mitigation model built with OSeMOSYS to quantify the costs and benefits of implementing LTS mitigation actions across different scenarios and futures. For adaptation, we developed a separate model using an open-access cost-benefit analysis tool to evaluate the economic impacts of sector-specific adaptation strategies aligned with the LTS, also incorporating uncertainty analysis through RDM. Mitigation Model ------------------------------- First, it is important to consider the workflow shown in Figure 1. This workflow indicates which files are important for executing each step and provides a better general understanding. .. figure:: _static/_images/flujo_trabajo.png :alt: Models used on the cost and benefits analysis :width: 100% :align: center **Figure 19:** Workflow of the OSeMOSYS-ECU model **Create the model structure (A1)** The first step of OSeMOSYS-ECU is to create the model structure. To do this, you need to run the Python script ``A1_Model_Structure``. To run this script, it is necessary to parameterize the Excel files inside ``A1_Inputs``: - ``A-I_Classifier_Modes_Demand`` - ``A-I_Classifier_Modes_Supply`` - ``A-I_Classifier_Modes_Transport`` - ``A-I_Horizon_Configuration`` Then, you must run the Python script ``A1_Model_Structure``. After the execution is complete, some files will be generated inside ``A1_Outputs``: - ``A-O_AR_Model_Base_Year.xlsx`` - ``A-O_AR_Projections.xlsx`` - ``A-O_Demand.xlsx`` - ``A-O_Fleet.xlsx`` - ``A-O_Parametrization.xlsx`` - ``A-O_Fleet_Groups.pickle`` These files are overwritten with the default structure each time the Python script is run, so it is recommended to run this script only once. **Model compiler (A2)** The second step consists of defining the process to compile the model into parameter files. To do this, it takes as inputs the Excel files from ``A1_Outputs``, as well as the Excel files from the ``A2_Xtra_Inputs`` folder and the file ``A2_Structure_Lists``. Then, run this Python script. It is important to have in the folder ``A2_Outputs_Params/Default``, the default files by parameter used by the Python script ``A2_Compiler``. This script generates some files in the ``A1_Outputs`` and ``A2_Outputs_Params`` folders. In the second folder, the same number of subfolders as there are scenarios in the model is generated, and inside these subfolders, Excel files with data by parameter are found. **Create the input file (B1)** The next step is longer and requires care. It is important to follow the workflow in the figure at the beginning of the section. First, go to the folder ``B1_Output_Params`` and delete any subfolder you find there. Then, go to the folder ``A2_Outputs_Params``, copy the folders with the names of the scenarios, and paste them into ``B1_Output_Params``. It is also necessary to manually copy the data from the file ``A2_Structure_Lists.xlsx`` to the file ``B1_Model_Structure``. Next, you must parameterize the model in the files ``B1_Scenario_Config.xlsx``. To write the model, use the script ``B1_Base_Scenarios_Adj_Parallel.py``. The results of this execution are found in the folder ``B1_Output_Params``. The files in this folder overwrite those from the ``A2`` step outputs. Additionally, the model file is located in the ``Executables`` folder, inside a subfolder for each scenario. This file is a text file, for example: - ``BAU_0.txt`` **Model execution (B1)** To run the model, use the script ``B1_Base_Scenarios_Adj_Parallel.py``. The results of this execution are found in the ``Executables`` folder, inside a subfolder for each scenario, and generate three files. **Results concatenation (B2)** This step facilitates the analysis of results. When running the Python script ``B2_Results_Creator_f0.py``, it takes the CSV files with input and output data of the model for each scenario, concatenates them, and creates four files: - ``Scenario_Name_Input.csv`` - ``Scenario_Name_Input_2024_10_22.csv`` - ``Scenario_Name_Output.csv`` - ``Scenario_Name_Output_2024_10_22.csv`` The files with dates allow tracking of executions made on different dates, as the files without dates are overwritten with each execution. Adaptation Model ------------------------------- **Overview** The *Metodología para la Priorización de Medidas de Adaptación frente al Cambio Climático* is a structured approach developed by GIZ to support decision-makers in evaluating and ranking adaptation measures. It is based on multi-criteria analysis (MCA) and participatory processes, ensuring that prioritized actions are effective, feasible, and aligned with local needs. **Steps for Using the Methodology** 1. **Define the Context** - Determine the geographical scope (national, regional, or local). - Identify the climate risks (e.g., droughts, floods) and sectors (e.g., agriculture, water resources). 2. **Identify Adaptation Measures** - Collect a list of potential adaptation actions from existing plans, expert input, or community consultations. - Include structural (infrastructure) and non-structural (policies, education) measures. 3. **Establish Evaluation Criteria** - Common criteria include: - **Effectiveness:** How well does the measure reduce climate risk? - **Feasibility:** Technical, social, and institutional capacity for implementation. - **Cost:** Initial investment and operational costs. - **Co-benefits:** Additional environmental, social, or economic benefits. - **Urgency & Equity:** How urgent is the measure, and who benefits? 4. **Engage Stakeholders** - Conduct workshops with stakeholders (government, civil society, technical experts). - Define and weight evaluation criteria based on local priorities. 5. **Score and Rank Measures** - Evaluate each adaptation measure against the selected criteria. - Apply weights to reflect the importance of each criterion. - Use scoring matrices (e.g., Excel-based tools) to calculate final scores. 6. **Interpret Results** - Generate a ranked list of prioritized measures. - Use this list to guide adaptation