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.
To plot your results, you may also load the package r2dii.plot.
We also recommend packages in the tidyverse; they are optional but useful.
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
#> ✔ tibble 3.1.3 ✔ dplyr 1.0.7
#> ✔ tidyr 1.1.3 ✔ stringr 1.4.0
#> ✔ readr 2.0.1 ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
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: 2,334 × 8
#> sector technology year region scenario_source metric production
#> <chr> <chr> <int> <chr> <chr> <chr> <dbl>
#> 1 automotive electric 2020 global demo_2020 projected 324592.
#> 2 automotive electric 2020 global demo_2020 target_cps 324592.
#> 3 automotive electric 2020 global demo_2020 target_sds 324592.
#> 4 automotive electric 2020 global demo_2020 target_sps 324592.
#> 5 automotive electric 2021 global demo_2020 projected 339656.
#> 6 automotive electric 2021 global demo_2020 target_cps 329191.
#> 7 automotive electric 2021 global demo_2020 target_sds 352505.
#> 8 automotive electric 2021 global demo_2020 target_sps 330435.
#> 9 automotive electric 2022 global demo_2020 projected 354720.
#> 10 automotive electric 2022 global demo_2020 target_cps 333693.
#> # … with 2,324 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: 32,946 × 9
#> sector technology year region scenario_source name_ald metric production
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <dbl>
#> 1 automotive electric 2020 global demo_2020 toyota … proje… 324592.
#> 2 automotive electric 2020 global demo_2020 toyota … targe… 324592.
#> 3 automotive electric 2020 global demo_2020 toyota … targe… 324592.
#> 4 automotive electric 2020 global demo_2020 toyota … targe… 324592.
#> 5 automotive electric 2021 global demo_2020 toyota … proje… 339656.
#> 6 automotive electric 2021 global demo_2020 toyota … targe… 329191.
#> 7 automotive electric 2021 global demo_2020 toyota … targe… 352505.
#> 8 automotive electric 2021 global demo_2020 toyota … targe… 330435.
#> 9 automotive electric 2022 global demo_2020 toyota … proje… 354720.
#> 10 automotive electric 2022 global demo_2020 toyota … targe… 333693.
#> # … with 32,936 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: 74 × 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 64 more rows
Visualization
There are a large variety of possible visualizations stemming from the outputs of target_market_share()
and target_sda()
. Below, we highlight a couple of common plots that can easily be created using the r2dii.plot
package.
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.
data <- filter(
market_share_targets_portfolio,
scenario_source == "demo_2020",
sector == "power",
region == "global",
metric %in% c("projected", "corporate_economy", "target_sds")
)
# Plot the technology mix
qplot_techmix(data)
#> Removing data before 2020 -- the start year of 'projected'.
#> The `technology_share` values are plotted for extreme years.
#> Do you want to plot different years? E.g. filter data with:`subset(data, year %in% c(2020, 2030))`.

Market Share: Technology-level volume trajectory
You can also plot the technology-specific volume trend. All starting values are normalized to 1, to emphasize that we are comparing the rates of buildout and/or retirement.
data <- filter(
market_share_targets_portfolio,
sector == "power",
technology == "renewablescap",
region == "global",
scenario_source == "demo_2020"
)
qplot_trajectory(data)
#> Removing data before 2020 -- the start year of 'projected'.
#> Normalizing `production` values to 2020 -- the start year.

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.
