consider reporting included sector/technology pairs in exported asset data in manifest

[cjyetman]

In order to properly specify the config files in workflow.transition.monitor or workflow.pacta, available sector/technology pairs in the exported asset data need to be known. It would be advantageous if these pairs were determined during the export process and included in the manifest to make this easier.

e.g.

library(tidyverse)

root_dir <- "~/Downloads"
timestamp_dir <- "2023Q4_20240228T144514Z"

equity_abcd_scenario <- readRDS(file.path(root_dir, timestamp_dir, "equity_abcd_scenario.rds"))
bonds_abcd_scenario <- readRDS(file.path(root_dir, timestamp_dir, "bonds_abcd_scenario.rds"))

equity_abcd_scenario %>% 
  select(ald_sector, technology) %>% 
  distinct() %>% 
  arrange(ald_sector, technology)
#> # A tibble: 17 × 2
#>    ald_sector technology          
#>    <chr>      <chr>               
#>  1 Automotive FuelCell            
#>  2 Automotive ICE                 
#>  3 Aviation   Freight             
#>  4 Aviation   Passenger           
#>  5 Cement     Integrated facility 
#>  6 Coal       Coal                
#>  7 Oil&Gas    Gas                 
#>  8 Oil&Gas    Oil                 
#>  9 Power      CoalCap             
#> 10 Power      GasCap              
#> 11 Power      HydroCap            
#> 12 Power      NuclearCap          
#> 13 Power      OilCap              
#> 14 Power      RenewablesCap       
#> 15 Steel      Basic Oxygen Furnace
#> 16 Steel      Electric Arc Furnace
#> 17 Steel      Open Hearth Furnace

bonds_abcd_scenario %>% 
  select(ald_sector, technology) %>% 
  distinct() %>% 
  arrange(ald_sector, technology)
#> # A tibble: 17 × 2
#>    ald_sector technology          
#>    <chr>      <chr>               
#>  1 Automotive FuelCell            
#>  2 Automotive ICE                 
#>  3 Aviation   Freight             
#>  4 Aviation   Passenger           
#>  5 Cement     Integrated facility 
#>  6 Coal       Coal                
#>  7 Oil&Gas    Gas                 
#>  8 Oil&Gas    Oil                 
#>  9 Power      CoalCap             
#> 10 Power      GasCap              
#> 11 Power      HydroCap            
#> 12 Power      NuclearCap          
#> 13 Power      OilCap              
#> 14 Power      RenewablesCap       
#> 15 Steel      Basic Oxygen Furnace
#> 16 Steel      Electric Arc Furnace
#> 17 Steel      Open Hearth Furnace

AB#10383

[cjyetman]

Also adding scenario source / scenario pairs would be helpful, to help avoid problems like this RMI-PACTA/workflow.transition.monitor#288

library(tidyverse)

root_dir <- "~/Documents/Data/workflow-data-preparation-outputs"
timestamp_dir <- "2023Q4_20240301T105304Z"

equity_abcd_scenario <- readRDS(file.path(root_dir, timestamp_dir, "equity_abcd_scenario.rds"))
bonds_abcd_scenario <- readRDS(file.path(root_dir, timestamp_dir, "bonds_abcd_scenario.rds"))

equity_scenario_source <-
  equity_abcd_scenario %>% 
  select(scenario_source, scenario) %>% 
  distinct() %>% 
  unite("scenario_source", scenario_source, scenario) %>% 
  arrange(scenario_source)

bonds_scenario_source <-
  bonds_abcd_scenario %>% 
  select(scenario_source, scenario) %>% 
  distinct() %>% 
  unite("scenario_source", scenario_source, scenario) %>% 
  arrange(scenario_source)

bind_rows(equity_scenario_source, bonds_scenario_source) %>% unique()
#> # A tibble: 7 × 1
#>   scenario_source   
#>   <chr>             
#> 1 GECO2023_1.5C     
#> 2 GECO2023_NDC-LTS  
#> 3 GECO2023_Reference
#> 4 ISF2023_1.5°C     
#> 5 WEO2023_APS       
#> 6 WEO2023_NZE       
#> 7 WEO2023_STEPS

equity_scenario_and_source <-
  equity_abcd_scenario %>% 
  select(scenario_source, scenario) %>% 
  distinct() %>% 
  arrange(scenario_source, scenario)

bonds_scenario_and_source <-
  bonds_abcd_scenario %>% 
  select(scenario_source, scenario) %>% 
  distinct() %>% 
  arrange(scenario_source, scenario)

bind_rows(equity_scenario_and_source, bonds_scenario_and_source) %>% unique()
#> # A tibble: 7 × 2
#>   scenario_source scenario 
#>   <chr>           <chr>    
#> 1 GECO2023        1.5C     
#> 2 GECO2023        NDC-LTS  
#> 3 GECO2023        Reference
#> 4 ISF2023         1.5°C    
#> 5 WEO2023         APS      
#> 6 WEO2023         NZE      
#> 7 WEO2023         STEPS