## SDA Methodology

The Sectoral Decarbonization Approach (SDA) is a method for setting corporate CO2 emissions intensity reduction targets in line with climate science. This method was developed by the Science-Based Targets Initiative (SBTI), an international initiative on science-based target setting for companies initiated by CDP, the United Nations Global Compact, the World Resources Institute (WRI), and the Worldwide Fund for Nature (WWF).

In the context of PACTA, this methodology is used to calculate emission factor targets for homogenous sectors (i.e. sectors with no technology-level scenario pathways).

First, the distance, $$d$$, between the company’s CO2 emissions intensity per unit production (or emissions factor), $$I^{Co}(t)$$ at some base year, $$t_0$$, and a scenario target intensity in 2050, $$I^{Sc}(2050)$$ is calculated. The target intensity in 2050 can be taken from any relevant climate scenario:

$d = I^{Co}(t_0) - I^{Sc}(2050)$

The company’s market share parameter, $$m(t)$$, is defined as the company’s expected future activity, $$P^{Co}(t)$$ divided by the sector’s future activity, $$P^{Sc}(t)$$ to reflect the expected forward-looking market share of the company. This is given as a ratio to the company’s base year market share, derived from its activity, $$P^{Co}(t_0)$$ divided by the sector’s activity in the same year, $$P^{Sc}(t_0)$$. In both cases the former is calculated per company, and the latter is determined from the climate scenario:

$m (t) = \dfrac{P^{Co}(t_0) / P^{Sc}(t_0)}{P^{Co}(t) / P^{Sc}(t)}$

It should be noted that this parameter does not capture the change in the market share of the company but rather the inverse. This is useful as it equates to a decreasing parameter when the company’s market share is increasing. This equates to larger reduction efforts when the companies market share is increasing over time.

The sector decarbonization factor, $$p(t)$$ is defined as:

$p(t) = \frac{I^{Sc}(t) - I^{Sc}(2050)}{I^{Sc}(t_0) - I^{Sc}(2050)}$

where $$I^{in}(t)$$ and $$I^{Sc}(t)$$ are the average market and scenario emission intensities respectively, at time $$t$$.

This variable captures the remaining effort needed from the market to meet the target in 2050, per year. Under the SDA assumptions the CO2 intensity for all companies in a sector converge in 2050. Note that $$p(t_0) = 1$$ and $$p(2050) = 0$$, indicating that 100% of the expected decarbonization efforts are still to be met at the base year and 0% should be left at 2050.

The company-level emission intensity target is then defined as: $I^{Target}(t) = \left( d * p (t) * m (t) \right) + I^{Sc}(2050)$

## PACTA Assumptions

The SDA applied in PACTA differs slightly from the way it is applied by the SBTI. In particular, we must align the top-down approach laid out by climate scenarios with the bottom-up asset-level data used in the PACTA analysis.

Assumption: Market share stays constant ($$m(t)$$ = 1)

Due to the lack of quantitative data on the expected market share changes throughout the entire time horizon up to 2050. $$m(t)$$ is set to 1 for all years. Under the SBTI method for calculating $$m(t)$$, there will be a higher intensity reduction target in cases where the absolute pathway of the sector exceeds the scenario target. This makes sense. However, applying this at company level is counter-intuitive:

Companies that decrease their market share would be allowed to have a higher CO2-Intensity than the average market actor. While, companies that are increasing their market share are forced to do more in terms of CO2-Intensity than ones whose market share remains constant. It follows that if a company reaches the targeted CO2-Intensity it would not be allowed to increase its share in the market. This is a desirable outcome.

Under this assumption, our target calculation reduces to:

$I^{Target}(t) = \left( d * p (t) \right) + I^{Sc}(2050)$

Approximation: Adjust base year scenario emission intensity

In both the SBTI and the PACTA methodology the target emissions for the sector are taken from climate scenarios. These implement a global economy top-down approach which applies an absolute emissions value in the year 2050 and then converts this to yearly emission intensities. However, there may be discrepancies between the Scenario projected emission intensities, and the bottom-up ALD emission intensities. To reflect this discrepancy, we adjust the scenario projections by the following factor,

$\dfrac{I^{ALD}(t_0)}{I^{Sc}(t_0)}$ yielding the adjusted scenario pathway:

$I'^{Sc}(t) = \left(\dfrac{I^{ALD}(t_0)}{I^{Sc}(t_0)}\right) * I^{Sc}(t)$ This yields the final PACTA SDA target equation:

$I^{Target}(t) = \left( d * p (t) \right) + I'^{Sc}(t)$ Note: $$d$$ and $$p(t)$$ also must be re-calculated using this adjusted scenario intensity, $$I'^{Sc}$$.

## Calculating SDA Targets

To calculate SDA targets you need to use the package r2dii.analysis and a number of datasets, including a “matched” dataset (loanbook + asset-level data) that you can get with the package r2dii.match. The datasets I use here come from the package r2dii.data; they are fake but show how you should structure your own data.

• Use packages.
library(r2dii.data)
library(r2dii.match)
library(r2dii.analysis)
• Match the loanbook to asset level data.
loanbook <- r2dii.data::loanbook_demo
ald <- r2dii.data::ald_demo

matched <- match_name(loanbook, ald) %>%
# WARNING: Remember to validate the output of match_name() before prioritize()
prioritize()

matched
#> # A tibble: 217 × 28
#>    id_loan id_direct_loanta… name_direct_loan… id_intermediate… name_intermedia…
#>    <chr>   <chr>             <chr>             <chr>            <chr>
#>  1 L6      C304              Yukon Developmen… <NA>             <NA>
#>  2 L13     C297              Yuba City Cogene… <NA>             <NA>
#>  3 L20     C287              Ytl Powerseraya … <NA>             <NA>
#>  4 L21     C286              Ytl Power Intern… <NA>             <NA>
#>  5 L22     C285              Ytl Corp Bhd      <NA>             <NA>
#>  6 L23     C283              Ypic Internation… <NA>             <NA>
#>  7 L24     C282              Ypfb Corporacion  <NA>             <NA>
#>  8 L25     C281              Ypf Sa            <NA>             <NA>
#>  9 L26     C280              Ypf Energia Elec… <NA>             <NA>
#> 10 L27     C278              Younicos Ag       <NA>             <NA>
#> # … with 207 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>, …
• Calculate SDA targets for CO2 emissions intensities:
co2_intensity <- r2dii.data::co2_intensity_scenario_demo

matched %>% target_sda(ald, co2_intensity)
#> Warning: Removing ald rows where emission_factor is NA
#> # A tibble: 163 × 4
#>    sector  year emission_factor_metric emission_factor_value
#>    <chr>  <dbl> <chr>                                  <dbl>
#>  1 cement  2013 projected                              0.658
#>  2 cement  2014 projected                              0.659
#>  3 cement  2015 projected                              0.660
#>  4 cement  2016 projected                              0.661
#>  5 cement  2017 projected                              0.662
#>  6 cement  2018 projected                              0.662
#>  7 cement  2019 projected                              0.663
#>  8 cement  2020 projected                              0.664
#>  9 cement  2021 projected                              0.665
#> 10 cement  2022 projected                              0.666
#> # … with 153 more rows