Introduction to r2dii.analysis

Load your r2dii libraries

The first step in your analysis will be to load in the recommended r2dii packages into your current R session. r2dii.data includes fake data to help demonstrate the tool and r2dii.match provides functions to help you easily match your loanbook to asset-level data.

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

We also recommend packages in the tidyverse; they are optional but useful.

library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✔ ggplot2 3.3.2     ✔ purrr   0.3.4
#> ✔ tibble  3.0.3     ✔ dplyr   1.0.2
#> ✔ tidyr   1.1.2     ✔ stringr 1.4.0
#> ✔ readr   1.3.1     ✔ forcats 0.5.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

Match your loanbook to climate-related asset-level data

See r2dii.match for a more complete description of this process.

# Use these datasets to practice but eventually you should use your own data.
# The optional syntax `package::data` is to clarify where the data comes from.
loanbook <- r2dii.data::loanbook_demo
ald <- r2dii.data::ald_demo

matched <- match_name(loanbook, ald) %>% prioritize()

matched
#> # A tibble: 267 x 28
#>    id_loan id_direct_loant… name_direct_loa… id_intermediate… name_intermedia…
#>    <chr>   <chr>            <chr>            <chr>            <chr>           
#>  1 L6      C304             Yukon Developme… <NA>             <NA>            
#>  2 L13     C297             Yuba City Cogen… <NA>             <NA>            
#>  3 L20     C287             Ytl Powerseraya… <NA>             <NA>            
#>  4 L21     C286             Ytl Power Inter… <NA>             <NA>            
#>  5 L22     C285             Ytl Corp Bhd     <NA>             <NA>            
#>  6 L23     C283             Ypic Internatio… <NA>             <NA>            
#>  7 L24     C282             Ypfb Corporacion <NA>             <NA>            
#>  8 L25     C281             Ypf Sa           <NA>             <NA>            
#>  9 L26     C280             Ypf Energia Ele… <NA>             <NA>            
#> 10 L27     C278             Younicos Ag      <NA>             <NA>            
#> # … with 257 more rows, and 23 more variables: id_ultimate_parent <chr>,
#> #   name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> #   loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> #   loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> #   sector_classification_input_type <chr>,
#> #   sector_classification_direct_loantaker <dbl>, fi_type <chr>,
#> #   flag_project_finance_loan <chr>, name_project <lgl>,
#> #   lei_direct_loantaker <lgl>, isin_direct_loantaker <lgl>, id_2dii <chr>,
#> #   level <chr>, sector <chr>, sector_ald <chr>, name <chr>, name_ald <chr>,
#> #   score <dbl>, source <chr>, borderline <lgl>

Calculate targets

You can calculate scenario targets using two different approaches: Market Share Approach, or Sectoral Decarbonization Approach.

Market Share Approach

The Market Share Approach is used to calculate scenario targets for the production of a technology in a sector. For example, we can use this approach to set targets for the production of electric vehicles in the automotive sector. This approach is recommended for sectors where a granular technology scenario roadmap exists.

Targets can be set at the portfolio level:

# Use these datasets to practice but eventually you should use your own data.
scenario <- r2dii.data::scenario_demo_2020
regions <- r2dii.data::region_isos_demo

market_share_targets_portfolio <- matched %>%
  target_market_share(
    ald = ald,
    scenario = scenario,
    region_isos = regions
  )

market_share_targets_portfolio
#> # A tibble: 1,170 x 8
#>    sector technology  year region scenario_source metric production
#>    <chr>  <chr>      <int> <chr>  <chr>           <chr>       <dbl>
#>  1 autom… electric    2020 global demo_2020       proje…    145942.
#>  2 autom… electric    2020 global demo_2020       corpo…   8134869.
#>  3 autom… electric    2020 global demo_2020       targe…    145942.
#>  4 autom… electric    2020 global demo_2020       targe…    145942.
#>  5 autom… electric    2020 global demo_2020       targe…    145942.
#>  6 autom… electric    2021 global demo_2020       proje…    148212.
#>  7 autom… electric    2021 global demo_2020       corpo…   8183411.
#>  8 autom… electric    2021 global demo_2020       targe…    148361.
#>  9 autom… electric    2021 global demo_2020       targe…    160625.
#> 10 autom… electric    2021 global demo_2020       targe…    149016.
#> # … with 1,160 more rows, and 1 more variable: technology_share <dbl>

Or at the company level:

market_share_targets_company <- matched %>%
  target_market_share(
    ald = ald,
    scenario = scenario,
    region_isos = regions,
    # Output results at company-level.
    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`?

market_share_targets_company
#> # A tibble: 15,945 x 9
#>    sector technology  year region scenario_source name_ald metric production
#>    <chr>  <chr>      <int> <chr>  <chr>           <chr>    <chr>       <dbl>
#>  1 autom… electric    2020 global demo_2020       shangha… proje…      5140.
#>  2 autom… electric    2020 global demo_2020       shangha… corpo…   8134869.
#>  3 autom… electric    2020 global demo_2020       shangha… targe…      5140.
#>  4 autom… electric    2020 global demo_2020       shangha… targe…      5140.
#>  5 autom… electric    2020 global demo_2020       shangha… targe…      5140.
#>  6 autom… electric    2020 global demo_2020       sichuan… proje…      2992.
#>  7 autom… electric    2020 global demo_2020       sichuan… corpo…   8134869.
#>  8 autom… electric    2020 global demo_2020       sichuan… targe…      2992.
#>  9 autom… electric    2020 global demo_2020       sichuan… targe…      2992.
#> 10 autom… electric    2020 global demo_2020       sichuan… targe…      2992.
#> # … with 15,935 more rows, and 1 more variable: technology_share <dbl>

Sectoral Decarbonization Approach

The Sectoral Decarbonization Approach is used to calculate scenario targets for the emission_factor of a sector. For example, you can use this approach to set targets for the average emission factor of the cement sector. This approach is recommended for sectors lacking technology roadmaps.

# Use this dataset to practice but eventually you should use your own data.
co2 <- r2dii.data::co2_intensity_scenario_demo

sda_targets <- matched %>%
  target_sda(ald = ald, co2_intensity_scenario = co2) %>% 
  filter(sector == "cement", year >= 2020)
#> Warning: Removing ald rows where `emission_factor` is NA

sda_targets
#> # A tibble: 29 x 4
#>    sector  year emission_factor_metric emission_factor_value
#>    <chr>  <dbl> <chr>                                  <dbl>
#>  1 cement  2020 projected                              0.664
#>  2 cement  2021 projected                              0.665
#>  3 cement  2022 projected                              0.666
#>  4 cement  2023 projected                              0.667
#>  5 cement  2024 projected                              0.668
#>  6 cement  2025 projected                              0.669
#>  7 cement  2020 corporate_economy                      0.669
#>  8 cement  2021 corporate_economy                      0.670
#>  9 cement  2022 corporate_economy                      0.671
#> 10 cement  2023 corporate_economy                      0.672
#> # … with 19 more rows

Visualization

There are a large variety of possible visualizations stemming from the outputs of target_market_share() and target_sda(). Below, we have highlighted a couple of common plots, along with the minimum code necessary to reproduce them.

Market Share: Sector-level technology mix

From the market share output, you can plot the portfolio’s exposure to various climate sensitive technologies (projected), and compare with the corporate economy, or against various scenario targets.

# Pick the targets you want to plot.
tech_mix_data <- market_share_targets_portfolio %>%
  filter(
    sector == "power",
    year == max(year)
  )

ggplot(
  data = tech_mix_data, 
  mapping = aes(
    x = metric, 
    y = technology_share, 
    fill = technology
  )
) +
  geom_col(position = "fill") +
  labs(x = "Metric", y = "Weighted Capacity [%]")

Market Share: Technology-level volume trajectory

You can also plot the technology-specific volume trend. All benchmarks must first be normalized to the initial portfolio value, so that we are effectively comparing ambition.

  market_share_targets_portfolio <- market_share_targets_portfolio %>% 
  select(-technology_share) %>% 
  pivot_wider(names_from = metric, 
              values_from = production) %>% 
  arrange(year) %>% 
  group_by(sector, technology, region, scenario_source) %>% 
  mutate(normalized_corporate_economy = corporate_economy * (first(projected)/first(corporate_economy))) %>% 
  select(-corporate_economy) %>% 
  pivot_longer(names_to = "metric",
               cols = projected:normalized_corporate_economy,
               values_to = "production")

renewables_data <- filter(
  market_share_targets_portfolio,
  sector == "power",
  technology == "renewablescap",
  region == "global"
)

just_targets <- renewables_data %>%
  filter(str_detect(metric, "target_"))

ceiling <- just_targets %>%
  group_by(sector, technology, year, region, scenario_source) %>%
  summarize(
    metric = "target_ceiling",
    production = max(production)
  ) %>%
  group_by(sector, technology, region, scenario_source) %>%
  mutate(production = max(production))
#> `summarise()` regrouping output by 'sector', 'technology', 'year', 'region' (override with `.groups` argument)

just_data <- renewables_data %>%
  filter(!str_detect(metric, "target_"))

targets_and_ceiling <- just_targets %>%
  bind_rows(ceiling) %>%
  group_by(year) %>%
  arrange(year, production) %>%
  mutate(
    previous_value = lag(production, default = 0)
  ) %>%
  filter(region == "global")

ggplot(
  data = targets_and_ceiling,
  mapping = aes(
    x = year,
    y = production,
    fill = metric
  )
) +
  geom_ribbon(
    data = targets_and_ceiling,
    mapping = aes(ymin = previous_value, ymax = production)
  ) +
  geom_line(
    data = just_data, 
    mapping = aes(
      x = year, 
      y = production, 
      linetype = metric
    )
  ) +
  facet_wrap(vars(sector, technology))

SDA Target

From the SDA output, we can compare the projected average emission intensity attributed to the portfolio, with the actual emission intensity scenario, and the scenario compliant SDA pathway that the portfolio must follow to achieve the scenario ambition by 2050.

ggplot(
  data = sda_targets, 
  mapping = aes(
    x = year, 
    y = emission_factor_value, 
    color = emission_factor_metric)
) +
  geom_line() +
  facet_wrap(~ sector)