r2dii.analysis下載 - r2dii.analysis原始碼下載

r2dii分析

這些工具可協助您評估金融投資組合是否符合氣候目標。他們總結了投資組合的關鍵指標（例如產量、排放因子），並根據氣候情境計算目標。他們在 R 中實現了免費軟體「PACTA」（《巴黎協定資本轉型評估》；https://www.transitionmonitor.com/）的最後一步。金融機構使用「PACTA」來研究其資本配置如何影響氣候。

安裝

從 CRAN 安裝 r2dii.analysis 的發布版本：

install.packages( " r2dii.analysis " )

或從 GitHub 安裝 r2dii.analysis 的開發版本：

 # install.packages("pak")
pak :: pak( " RMI-PACTA/r2dii.analysis " )

例子

使用library()附加您需要的套件。 r2dii.analysis 不依賴 r2dii.data 和 r2dii.match 套件；但我們建議您使用install.packages(c("r2dii.data", "r2dii.match"))來安裝它們 - 這樣您就可以重現我們的範例。

library( r2dii.data )
library( r2dii.match )
library( r2dii.analysis )

使用r2dii.match::match_name()來識別貸款簿和資產等級資料之間的匹配。

 matched <- match_name( loanbook_demo , abcd_demo ) % > %
  prioritize()

新增場景目標

使用target_sda()計算 CO2 排放的 SDA 目標。

 matched % > %
  target_sda(
    abcd = abcd_demo ,
    co2_intensity_scenario = co2_intensity_scenario_demo ,
    region_isos = region_isos_demo
  )
# > Warning: Removing rows in abcd where `emission_factor` is NA
# > # A tibble: 220 × 6
# >    sector  year region             scenario_source emission_factor_metric
# >    <chr>  <dbl> <chr>              <chr>           <chr>                 
# >  1 cement  2020 advanced economies demo_2020       projected             
# >  2 cement  2020 developing asia    demo_2020       projected             
# >  3 cement  2020 global             demo_2020       projected             
# >  4 cement  2021 advanced economies demo_2020       projected             
# >  5 cement  2021 developing asia    demo_2020       projected             
# >  6 cement  2021 global             demo_2020       projected             
# >  7 cement  2022 advanced economies demo_2020       projected             
# >  8 cement  2022 developing asia    demo_2020       projected             
# >  9 cement  2022 global             demo_2020       projected             
# > 10 cement  2023 advanced economies demo_2020       projected             
# > # ℹ 210 more rows
# > # ℹ 1 more variable: emission_factor_value <dbl>

使用target_market_share計算投資組合層級的市佔率場景目標：

 matched % > %
  target_market_share(
    abcd = abcd_demo ,
    scenario = scenario_demo_2020 ,
    region_isos = region_isos_demo
  )
# > # A tibble: 1,076 × 10
# >    sector     technology  year region scenario_source metric     production
# >    <chr>      <chr>      <int> <chr>  <chr>           <chr>           <dbl>
# >  1 automotive electric    2020 global demo_2020       projected     145649.
# >  2 automotive electric    2020 global demo_2020       target_cps    145649.
# >  3 automotive electric    2020 global demo_2020       target_sds    145649.
# >  4 automotive electric    2020 global demo_2020       target_sps    145649.
# >  5 automotive electric    2021 global demo_2020       projected     147480.
# >  6 automotive electric    2021 global demo_2020       target_cps    146915.
# >  7 automotive electric    2021 global demo_2020       target_sds    153332.
# >  8 automotive electric    2021 global demo_2020       target_sps    147258.
# >  9 automotive electric    2022 global demo_2020       projected     149310.
# > 10 automotive electric    2022 global demo_2020       target_cps    148155.
# > # ℹ 1,066 more rows
# > # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
# > #   percentage_of_initial_production_by_scope <dbl>

或在公司層面：

 matched % > %
  target_market_share(
    abcd = abcd_demo ,
    scenario = scenario_demo_2020 ,
    region_isos = region_isos_demo ,
    by_company = TRUE
  )
# > Warning: You've supplied `by_company = TRUE` and `weight_production = TRUE`.
# > This will result in company-level results, weighted by the portfolio
# > loan size, which is rarely useful. Did you mean to set one of these
# > arguments to `FALSE`?
# > # A tibble: 14,505 × 11
# >    sector    technology  year region scenario_source name_abcd metric production
# >    <chr>     <chr>      <int> <chr>  <chr>           <chr>     <chr>       <dbl>
# >  1 automoti… electric    2020 global demo_2020       Bernardi… proje…     17951.
# >  2 automoti… electric    2020 global demo_2020       Bernardi… targe…     17951.
# >  3 automoti… electric    2020 global demo_2020       Bernardi… targe…     17951.
# >  4 automoti… electric    2020 global demo_2020       Bernardi… targe…     17951.
# >  5 automoti… electric    2020 global demo_2020       Christia… proje…     11471.
# >  6 automoti… electric    2020 global demo_2020       Christia… targe…     11471.
# >  7 automoti… electric    2020 global demo_2020       Christia… targe…     11471.
# >  8 automoti… electric    2020 global demo_2020       Christia… targe…     11471.
# >  9 automoti… electric    2020 global demo_2020       Donati, … proje…      5611.
# > 10 automoti… electric    2020 global demo_2020       Donati, … targe…      5611.
# > # ℹ 14,495 more rows
# > # ℹ 3 more variables: technology_share <dbl>, scope <chr>,
# > #   percentage_of_initial_production_by_scope <dbl>

實用功能

target_*()函數提供常見操作的捷徑。它們封裝了一些您也可以直接使用的實用函數：

使用join_abcd_scenario()將匹配的數據集連接到相關場景數據，並選擇相關區域中的資產。

 loanbook_joined_to_abcd_scenario <- matched % > %
  join_abcd_scenario(
    abcd = abcd_demo ,
    scenario = scenario_demo_2020 ,
    region_isos = region_isos_demo
  )

使用不同分組參數的summarize_weighted_production()來計算場景目標：

 # portfolio level
loanbook_joined_to_abcd_scenario % > %
  summarize_weighted_production( scenario , tmsr , smsp , region )
# > # A tibble: 756 × 9
# >    sector_abcd technology  year scenario  tmsr    smsp region
# >    <chr>       <chr>      <int> <chr>    <dbl>   <dbl> <chr> 
# >  1 automotive  electric    2020 cps       1    0       global
# >  2 automotive  electric    2020 sds       1    0       global
# >  3 automotive  electric    2020 sps       1    0       global
# >  4 automotive  electric    2021 cps       1.12 0.00108 global
# >  5 automotive  electric    2021 sds       1.16 0.00653 global
# >  6 automotive  electric    2021 sps       1.14 0.00137 global
# >  7 automotive  electric    2022 cps       1.24 0.00213 global
# >  8 automotive  electric    2022 sds       1.32 0.0131  global
# >  9 automotive  electric    2022 sps       1.29 0.00273 global
# > 10 automotive  electric    2023 cps       1.35 0.00316 global
# > # ℹ 746 more rows
# > # ℹ 2 more variables: weighted_production <dbl>,
# > #   weighted_technology_share <dbl>

# company level
loanbook_joined_to_abcd_scenario % > %
  summarize_weighted_production( scenario , tmsr , smsp , region , name_abcd )
# > # A tibble: 13,023 × 10
# >    sector_abcd technology  year scenario  tmsr  smsp region name_abcd           
# >    <chr>       <chr>      <int> <chr>    <dbl> <dbl> <chr>  <chr>               
# >  1 automotive  electric    2020 cps          1     0 global Bernardi, Bernardi …
# >  2 automotive  electric    2020 cps          1     0 global Christiansen PLC    
# >  3 automotive  electric    2020 cps          1     0 global Donati, Donati e Do…
# >  4 automotive  electric    2020 cps          1     0 global DuBuque-DuBuque     
# >  5 automotive  electric    2020 cps          1     0 global Ferrari-Ferrari SPA 
# >  6 automotive  electric    2020 cps          1     0 global Ferry and Sons      
# >  7 automotive  electric    2020 cps          1     0 global Goyette-Goyette     
# >  8 automotive  electric    2020 cps          1     0 global Guerra, Guerra e Gu…
# >  9 automotive  electric    2020 cps          1     0 global Gutkowski, Gutkowsk…
# > 10 automotive  electric    2020 cps          1     0 global Hilpert, Hilpert an…
# > # ℹ 13,013 more rows
# > # ℹ 2 more variables: weighted_production <dbl>,
# > #   weighted_technology_share <dbl>