Initial value of technology_share differs between projected and target_*

[jdhoffa]

The technology_share should be identical between the projected and target_* values at the start year of the analysis. In the reprex below, we can see that this is not the case.

I have a hunch as to why this is, I believe the target technology_share is being calculated the old way (dividing weighted_production by the sum of weighted_production). Need to explore how to solve this.

Thanks as always @georgeharris2deg

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(r2dii.data)
library(r2dii.analysis)

matched <- tibble::tribble(
  ~id_loan, ~loan_size_outstanding, ~loan_size_outstanding_currency, ~loan_size_credit_limit, ~loan_size_credit_limit_currency, ~id_2dii,            ~level, ~score,      ~sector,   ~name_ald,  ~sector_ald,
  "L1",                      1,                           "EUR",                       2,                            "EUR",    "UP1", "ultimate_parent",      1, "automotive", "company a", "automotive",
  "L2",                      1,                           "EUR",                       2,                            "EUR",    "UP1", "ultimate_parent",      1, "automotive", "company b", "automotive"
)

ald <- tibble::tribble(
  ~name_company,      ~sector, ~technology, ~year, ~production, ~emission_factor, ~plant_location, ~is_ultimate_owner,
  "company a", "automotive",       "ice",  2020,         100,                1,            "BF",               TRUE,
  "company b", "automotive",       "ice",  2020,           1,                1,            "BF",               TRUE,
  "company a", "automotive",  "electric",  2020,         100,                1,            "BF",               TRUE,
  "company b", "automotive",  "electric",  2020,           3,                1,            "BF",               TRUE
)

scenario <- tibble::tribble(
  ~scenario,      ~sector, ~technology,  ~region, ~year, ~tmsr, ~smsp, ~scenario_source,
  "sds", "automotive",       "ice", "global",  2020,     1,     0,      "demo_2020",
  "sds", "automotive",  "electric", "global",  2020,     1,     0,      "demo_2020"
)

out <- target_market_share(matched, ald, scenario, region_isos_demo)

out %>% 
  filter(
    metric %in% c("projected", "target_sds"),
    year == 2020
    ) %>% 
  select(technology, metric, technology_share)
#> # A tibble: 4 x 3
#>   technology metric     technology_share
#>   <chr>      <chr>                 <dbl>
#> 1 electric   projected             0.625
#> 2 electric   target_sds            0.505
#> 3 ice        projected             0.375
#> 4 ice        target_sds            0.495

^{Created on 2021-02-11 by the reprex package (v1.0.0)}

[jdhoffa]

Getting into the guts of this problem, it seems that the first step will be a rather large refactoring of target_market_share.
I want to keep track of my thoughts here, my goal is to:

• Refactor target_market_share so that the order of operations is: "calculate ald targets at company-level", "weight values (or not)", "calculate tech_share", "summarize (or not)". Open PR with just this, and ensure existing tests pass, with the incorrect tech_share
• Implement correct calculation of tech_share targets.