These tools help you to assess if a financial portfolio aligns with climate goals. They summarize key metrics attributed to the portfolio (e.g. production, emission factors), and calculate targets based on climate scenarios. They implement in R the last step of the free software ‘PACTA’ (Paris Agreement Capital Transition Assessment; https://2degrees-investing.org/). Financial institutions use ‘PACTA’ to study how their capital allocation impacts the climate.
Install the development version of r2dii.analysis with:
library() to attach the packages you need. r2dii.analysis does not depend on the packages r2dii.data and r2dii.match; but we suggest you install them – with install.packages(c("r2dii.data", "r2dii.match")) – so you can reproduce our examples.r2dii.match::match_name() to identify matches between your loanbook and the asset level data.target_sda() to calculate SDA targets of CO2 emissions.matched %>%
target_sda(
ald = ald_demo,
co2_intensity_scenario = co2_intensity_scenario_demo
)
#> # A tibble: 28 x 4
#> # Groups: sector [1]
#> sector year emission_factor_metric emission_factor_value
#> <chr> <dbl> <chr> <dbl>
#> 1 cement 2020 projected 0.664
#> 2 cement 2020 target 0.669
#> 3 cement 2020 scenario_benchmark 0.7
#> 4 cement 2021 projected 0.665
#> 5 cement 2021 target 0.612
#> 6 cement 2021 scenario_benchmark 0.64
#> 7 cement 2022 projected 0.666
#> 8 cement 2022 target 0.555
#> 9 cement 2022 scenario_benchmark 0.580
#> 10 cement 2023 projected 0.667
#> # … with 18 more rowstarget_market_share to calculate market-share scenario targets at the portfolio level:matched %>%
target_market_share(
ald = ald_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo
)
#> # A tibble: 1,170 x 7
#> sector technology year region scenario_source weighted_produc…
#> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 autom… electric 2020 global demo_2020 projected
#> 2 autom… electric 2020 global demo_2020 normalized_corp…
#> 3 autom… electric 2020 global demo_2020 target_cps
#> 4 autom… electric 2020 global demo_2020 target_sds
#> 5 autom… electric 2020 global demo_2020 target_sps
#> 6 autom… hybrid 2020 global demo_2020 projected
#> 7 autom… hybrid 2020 global demo_2020 normalized_corp…
#> 8 autom… hybrid 2020 global demo_2020 target_cps
#> 9 autom… hybrid 2020 global demo_2020 target_sds
#> 10 autom… hybrid 2020 global demo_2020 target_sps
#> # … with 1,160 more rows, and 1 more variable: weighted_production_value <dbl>matched %>%
target_market_share(
ald = ald_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo,
by_company = TRUE
)
#> # A tibble: 15,945 x 8
#> sector technology year region scenario_source name_ald weighted_produc…
#> <chr> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 autom… electric 2020 global demo_2020 shangha… projected
#> 2 autom… electric 2020 global demo_2020 shangha… normalized_corp…
#> 3 autom… electric 2020 global demo_2020 shangha… target_cps
#> 4 autom… electric 2020 global demo_2020 shangha… target_sds
#> 5 autom… electric 2020 global demo_2020 shangha… target_sps
#> 6 autom… electric 2020 global demo_2020 sichuan… projected
#> 7 autom… electric 2020 global demo_2020 sichuan… normalized_corp…
#> 8 autom… electric 2020 global demo_2020 sichuan… target_cps
#> 9 autom… electric 2020 global demo_2020 sichuan… target_sds
#> 10 autom… electric 2020 global demo_2020 sichuan… target_sps
#> # … with 15,935 more rows, and 1 more variable: weighted_production_value <dbl>The target_*() functions provide shortcuts for common operations. They wrap some utility functions that you may also use directly:
join_ald_scenario() to join a matched dataset to the relevant scenario data, and to pick assets in the relevant regions.loanbook_joined_to_ald_scenario <- matched %>%
join_ald_scenario(
ald = ald_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo
)summarize_weighted_production() with different grouping arguments to calculate scenario-targets:# portfolio level
loanbook_joined_to_ald_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region)
#> # A tibble: 702 x 8
#> sector technology year scenario tmsr smsp region weighted_production
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 automotive electric 2020 cps 1 0 global 148935.
#> 2 automotive electric 2020 sds 1 0 global 148935.
#> 3 automotive electric 2020 sps 1 0 global 148935.
#> 4 automotive electric 2021 cps 1.12 0.00108 global 150875.
#> 5 automotive electric 2021 sds 1.16 0.00653 global 150875.
#> 6 automotive electric 2021 sps 1.14 0.00137 global 150875.
#> 7 automotive electric 2022 cps 1.24 0.00213 global 152816.
#> 8 automotive electric 2022 sds 1.32 0.0131 global 152816.
#> 9 automotive electric 2022 sps 1.29 0.00273 global 152816.
#> 10 automotive electric 2023 cps 1.35 0.00316 global 154757.
#> # … with 692 more rows
# company level
loanbook_joined_to_ald_scenario %>%
summarize_weighted_production(scenario, tmsr, smsp, region, name_ald)
#> # A tibble: 9,567 x 9
#> sector technology year scenario tmsr smsp region name_ald weighted_produc…
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 autom… electric 2020 cps 1 0 global shangha… 5140.
#> 2 autom… electric 2020 cps 1 0 global sichuan… 5985.
#> 3 autom… electric 2020 cps 1 0 global singula… 8674.
#> 4 autom… electric 2020 cps 1 0 global south-e… 14409.
#> 5 autom… electric 2020 cps 1 0 global suzuki … 6019.
#> 6 autom… electric 2020 cps 1 0 global tata gr… 876.
#> 7 autom… electric 2020 cps 1 0 global tesla i… 6208.
#> 8 autom… electric 2020 cps 1 0 global toyota … 19860.
#> 9 autom… electric 2020 cps 1 0 global volkswa… 9258.
#> 10 autom… electric 2020 cps 1 0 global wheego 9804.
#> # … with 9,557 more rows