Overview ticket of left-overs for Research Pipeline

[AnneSchoenauer]

Fixing bugs & recalculate results

• Please fix the NA issues - this ticket here
• Excluding products: please set all ecoinvent matches NA which are in the tilt sector 'power', 'energy' and 'construction' (here)
• Excluding isic codes: here
• exclude outliers
• fixing the ranking of reference groups for 'unit', 'tilt_subsector', 'tilt_subsector_unit', 'isic_4digit', 'isic_4_digit_unit' so that it is implemented as in this reprex here.
• Can you please check if this issue also existed in the Bundesbank data?
• Check if the transition risk threshold changed - so this table Most likely they did. Could you please provide then a new table and set the tresholds differently for low, medium and high?

Exlcuding firms & products

• product-level: exclude products which don't have results
• firm-level: exlcude all firms that don't have an emission profile as done here

New data

• Save the data and share it with us --> @Tilmon this is then the new data that we share with externals when needed. I however wonder if we still could generate the anonymised data to update this on zenodo.

Descriptive analysis

• Please document everything in scripts that can be handed over to Anne
• Is it possible to share all csv files for creating the plots or the tables? So not only receiving the bar charts or the maps but also the underlying data underneath it? That would be great for proper review.

Decisions up-front

• Filter for the emission profile 'tilt_subsector_unit'
• Filter for the transition risk 'tilt_subsector_unit_1.5_RPS_2030' and 'tilt_subsector_unit_NZ2050_2030'. So please choose the tilt_subsector_unit and the WEO and IPR scenario both for year '2030'
• Please note that all old tables and graphs might have more scenarios and reference groups. But please only use the above to make it a little bit less complex.

Data Coverage
Analysis firm-level

• [Re-generate descriptives about the firms]([https://tiltsmes.sharepoint.com/:w:/s/product/EUqVB2Rk_zxFs720vgnt_tIB4eVqq_pedfvUEe7NMvz88A?e=dOzcCl&nav=eyJjIjozNzUyODYxOTh9]
• Re-generate descriptives about the products

Analysis relative emission intensity indicator on portfolio level

• Bar Chart for all countries with equal weight, best case and worst case (3 bar charts)
• Maps per countries with equal weight, best case and worst case (15 maps)
• COV
• average emission rank

Analysis transition risk indicator on portfolio level

• Bar Chart for all countries with equal weight, best case and worst case (3 bar charts)
• Maps per countries with equal weight, best case and worst case (15 maps)
• COV
• average transition risk score

Identify high relative emission intensive firms
Only GER portfolio(#820 (comment))

• Bar chart
• sector table --> please share the sector table so that we can make the decision on which 5 sectors we focus on for identifying the right firms (next step)
• Identifying the right firms (40 for each sector)
• Input products for the 200 firms.

Identify high transition risk firms
Only GER portfolio

• Bar chart
• sector table --> please share the sector table so that we can make the decision on which 5 sectors we focus on for identifying the right firms (next step)
• Identifying the right firms (40 for each sector)
• Input products for the 200 firms.

Appendix

• Sector distribution LCA

Proper handover of research pipeline

• Anne/Tilman can run all descritpive analyses by themselves
• Need a handover to run the research pipeline independently, incl. a handover of the two scripts needed to run the pipeline

[Tilmon]

UPDATE 2025-02-10

Estimated effort: apporx. 1.5 weeks, i.e. 19th of February. If this is going to take longer, let's discuss

[kalashsinghal]

Hi @AnneSchoenauer @Tilmon

Good News! The ranking bug was never present in the first place. Please have a look at the reprex which shows that the ranking is done correctly as we expect it to be:

library(readr)
library(dplyr)
devtools::load_all(".")
#> ℹ Loading tiltIndicator
options(width = 500)

profile_rank_sample <- example_data_factory(
  # styler: off
  tribble(
    ~activity_uuid_product_uuid, ~co2_footprint, ~tilt_subsector, ~unit, ~isic_4digit, ~tilt_sector, 
    "uuid_1",            1.0,      "land use",  "kg",     "'2345'",     "sector",
    "uuid_2",            2.0,      "land use",  "kg",     "'2345'",     "sector",
    "uuid_3",            3.0,         "power",  "kg",     "'2345'",     "sector",
    "uuid_4",            4.0,         "power",  "kg",     "'2345'",     "sector",
    "uuid_5",            5.0,         "power",  "m3",     "'2345'",     "sector",
    "uuid_6",            6.0,         "power",  "m3",     "'2345'",     "sector"
  )
  # styler: on
)


profile_rank_sample <- profile_rank_sample()
profile_rank_sample
#> # A tibble: 6 × 6
#>   activity_uuid_product_uuid co2_footprint tilt_subsector unit  isic_4digit tilt_sector
#>   <chr>                              <dbl> <chr>          <chr> <chr>       <chr>      
#> 1 uuid_1                                 1 land use       kg    '2345'      sector     
#> 2 uuid_2                                 2 land use       kg    '2345'      sector     
#> 3 uuid_3                                 3 power          kg    '2345'      sector     
#> 4 uuid_4                                 4 power          kg    '2345'      sector     
#> 5 uuid_5                                 5 power          m3    '2345'      sector     
#> 6 uuid_6                                 6 power          m3    '2345'      sector

out <- epa_compute_profile_ranking(profile_rank_sample)
out |> 
  print(n = Inf)
#> # A tibble: 36 × 8
#>    grouped_by          profile_ranking activity_uuid_product_uuid co2_footprint tilt_subsector unit  isic_4digit tilt_sector
#>    <chr>                         <dbl> <chr>                              <dbl> <chr>          <chr> <chr>       <chr>      
#>  1 all                           0.167 uuid_1                                 1 land use       kg    '2345'      sector     
#>  2 all                           0.333 uuid_2                                 2 land use       kg    '2345'      sector     
#>  3 all                           0.5   uuid_3                                 3 power          kg    '2345'      sector     
#>  4 all                           0.667 uuid_4                                 4 power          kg    '2345'      sector     
#>  5 all                           0.833 uuid_5                                 5 power          m3    '2345'      sector     
#>  6 all                           1     uuid_6                                 6 power          m3    '2345'      sector     
#>  7 isic_4digit                   0.167 uuid_1                                 1 land use       kg    '2345'      sector     
#>  8 isic_4digit                   0.333 uuid_2                                 2 land use       kg    '2345'      sector     
#>  9 isic_4digit                   0.5   uuid_3                                 3 power          kg    '2345'      sector     
#> 10 isic_4digit                   0.667 uuid_4                                 4 power          kg    '2345'      sector     
#> 11 isic_4digit                   0.833 uuid_5                                 5 power          m3    '2345'      sector     
#> 12 isic_4digit                   1     uuid_6                                 6 power          m3    '2345'      sector     
#> 13 tilt_subsector                0.5   uuid_1                                 1 land use       kg    '2345'      sector     
#> 14 tilt_subsector                1     uuid_2                                 2 land use       kg    '2345'      sector     
#> 15 tilt_subsector                0.25  uuid_3                                 3 power          kg    '2345'      sector     
#> 16 tilt_subsector                0.5   uuid_4                                 4 power          kg    '2345'      sector     
#> 17 tilt_subsector                0.75  uuid_5                                 5 power          m3    '2345'      sector     
#> 18 tilt_subsector                1     uuid_6                                 6 power          m3    '2345'      sector     
#> 19 unit                          0.25  uuid_1                                 1 land use       kg    '2345'      sector     
#> 20 unit                          0.5   uuid_2                                 2 land use       kg    '2345'      sector     
#> 21 unit                          0.75  uuid_3                                 3 power          kg    '2345'      sector     
#> 22 unit                          1     uuid_4                                 4 power          kg    '2345'      sector     
#> 23 unit                          0.5   uuid_5                                 5 power          m3    '2345'      sector     
#> 24 unit                          1     uuid_6                                 6 power          m3    '2345'      sector     
#> 25 unit_isic_4digit              0.25  uuid_1                                 1 land use       kg    '2345'      sector     
#> 26 unit_isic_4digit              0.5   uuid_2                                 2 land use       kg    '2345'      sector     
#> 27 unit_isic_4digit              0.75  uuid_3                                 3 power          kg    '2345'      sector     
#> 28 unit_isic_4digit              1     uuid_4                                 4 power          kg    '2345'      sector     
#> 29 unit_isic_4digit              0.5   uuid_5                                 5 power          m3    '2345'      sector     
#> 30 unit_isic_4digit              1     uuid_6                                 6 power          m3    '2345'      sector     
#> 31 unit_tilt_subsector           0.5   uuid_1                                 1 land use       kg    '2345'      sector     
#> 32 unit_tilt_subsector           1     uuid_2                                 2 land use       kg    '2345'      sector     
#> 33 unit_tilt_subsector           0.5   uuid_3                                 3 power          kg    '2345'      sector     
#> 34 unit_tilt_subsector           1     uuid_4                                 4 power          kg    '2345'      sector     
#> 35 unit_tilt_subsector           0.5   uuid_5                                 5 power          m3    '2345'      sector     
#> 36 unit_tilt_subsector           1     uuid_6                                 6 power          m3    '2345'      sector

^{Created on 2025-02-10 with reprex v2.1.1}

The reason why some tilt subsectors don't have a ranking 1 is that we rank the activity_uuid_product_uuids first (present in co2 table) and then match these uuids with the uuids present in the europages companies table. So, the uuids which are not present in the companies table got excluded from the output and hence we can't see the rank 1 which would belong to these excluded uuids. We are performing a left join between the two tables which removes the uuids not present in the europages table.

Does it makes sense?

[Tilmon]

@kalashsinghal thanks for providing this reprex and the explanation. Glad and relieved to hear :)