vignettes/production-percent-change.Rmd
production-percent-change.RmdFor most intents and purposes, we recommend calculating all targets using the loan weighted production as an indicator. In particular, we define the loan weighted production of a given company, \(j\) as: \[ \overline{p}_{i,j}(t) = p_{i,j}(t) * \dfrac{l_j}{\sum_j l_j}\] where \(p_{i,j}\) is the production of company \(i\) in technology \(j\) and \(l_j\) is the loan given to company \(j\).
To calculate portfolio targets, we aggregate this value by summing over every company in the portfolio: \[ \overline{p}_i (t) = \sum_j \left[ p_{i,j}(t) * \dfrac{l_j}{\sum_j l_j} \right] \]
Effectively, this is a loan-weighted average of the production attributed to each company in your portfolio. A significant result of this indicator choice is that small companies (with little production) will be favorably weighted, given that the loan to that company is sufficiently large. This can be useful to reflect large investments into green start-ups.
To calculate the weighted production:
library(r2dii.data)
library(r2dii.match)
library(r2dii.analysis)
master <- loanbook_demo %>%
match_name(ald_demo) %>%
prioritize() %>%
join_ald_scenario(
ald = ald_demo,
scenario = scenario_demo_2020,
region_isos = region_isos_demo
)
summarize_weighted_production(master)
#> # A tibble: 138 × 5
#> sector_ald technology year weighted_production weighted_technology_share
#> <chr> <chr> <int> <dbl> <dbl>
#> 1 automotive electric 2020 973775. 0.114
#> 2 automotive electric 2021 1018967. 0.118
#> 3 automotive electric 2022 1064159. 0.122
#> 4 automotive electric 2023 1109351. 0.126
#> 5 automotive electric 2024 1154543. 0.130
#> 6 automotive electric 2025 1199735. 0.133
#> 7 automotive electric 2026 1244926. 0.137
#> 8 automotive hybrid 2020 1886042. 0.221
#> 9 automotive hybrid 2021 1709634. 0.198
#> 10 automotive hybrid 2022 1533226. 0.176
#> # … with 128 more rowsOn the other-hand, if you’re more keen to understand if the large corporations in your portfolio are planning to make any significant changes, the percent change in production may be a more useful indicator.
For each company, we define the percent change, \(\chi_i(t)\), as compared to the start year, \(t_0\):
\[ \chi_i(t) = \dfrac{p_{i}(t)-p_{i}(t_0)}{p_i(t_0)} * 100\] where \(p_i(t)\) is the indicator (production or capacity) of technology \(i\), and \(t0\) is the start year of the analysis.
We aggregate the percent-change in production for each company to the portfolio-level, by using the same loan-weighted average as above. In particular, for each loan \(l_j\) to company \(j\), we have: \[ \overline{\chi_i} = \sum_j \left[ \chi_{i,j} * \dfrac{l_j}{\sum_j l_j} \right]\]
It should be noted that the percent change, \(\chi\), is undefined for 0 initial production. Intuitively, this makes sense, since you would require an “infinite percent” build-out to grow to anything from 0. For this reason, any company having 0 initial production is filtered out prior to calculating the percent change indicator.
To calculate the weighted percent change:
# using the master dataset defined in the previous chunk:
summarize_weighted_percent_change(master)
#> # A tibble: 138 × 4
#> sector_ald technology year weighted_percent_change
#> <chr> <chr> <int> <dbl>
#> 1 automotive electric 2020 0
#> 2 automotive electric 2021 0.0881
#> 3 automotive electric 2022 0.176
#> 4 automotive electric 2023 0.264
#> 5 automotive electric 2024 0.352
#> 6 automotive electric 2025 0.440
#> 7 automotive electric 2026 0.528
#> 8 automotive hybrid 2020 1.78
#> 9 automotive hybrid 2021 1.43
#> 10 automotive hybrid 2022 1.09
#> # … with 128 more rows