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GCAM-Europe Particularities

Source: vignettes/GCAMEurope.Rmd
GCAMEurope.Rmd

GCAM-Europe is an advanced regional expansion of the Global Change Analysis Model (GCAM). While the core version of GCAM aggregates the world into 32 regions, with the European continent divided into just five (EU-12, EU-15, Eastern Europe, Europe-non-EU, and EFTA), GCAM-Europe disaggregates 39 European countries into individual model regions. This high level of geographical resolution makes it possible to explore the country-level impacts of European policy packages and transformational strategies, alongside potential international spillover effects (e.g., carbon leakage) across 27 distinct non-European global regions. More details can be found in the upcoming publication!

Output Standardization Workaround

Due to an open rgcam issue (#93), output standardization requires manually handling four specific queries. You must extract and rename them as follows:

  • CO2 emissions by tech (nested subsector) (excluding resource production) →\rightarrow Rename to: CO2 emissions by tech (excluding resource production)
  • CO2 sequestration by tech (nested subsector) →\rightarrow Rename to: CO2 sequestration by tech
  • water withdrawals by subsector →\rightarrow Keep original name
  • water consumption by subsector →\rightarrow Keep original name

Follow the steps below to complete this manual integration:

  1. Initialize the project: Execute gcamreport in your R console, specifying either GCAM_version = "vEurope7.2" or GCAM_version = "vEurope8.7". This initializes the directory structure and begins the usual standardization process.

  2. Extract queries via Model Interface: Open the GCAM Model Interface and load the four queries listed above for all regions and your target scenarios.

  3. Format the CSV file: Copy the extracted datasets into a spreadsheet editor (e.g., Excel). Manually update the query names as required, and verify that no spurious commas exist within the query names or as ghost columns. Export the final sheet as a clean CSV file.

  4. Append data to the project: Once the initial gcamreport standardization process halts (which is expected to fail initially due to the missing queries), run the following line in your R console to inject your manually formatted .csv file:
    R prj <- rgcam::addMIBatchCSV(fn = "path/to/queries.csv", clobber = TRUE, saveProj = TRUE)

  5. Reset R session: Clear your global environment variables and restart/reset your R session to ensure no cached data interferes with the next step.

  6. Generate the finalized report: Run the standardization on the updated project. When calling the generate_report() function, pass your updated project via the prj_name argument. Do not specify db_path or db_name.