## Weighted Production

For 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 rows

## Weighted Percent Change in Production

On 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