In 2DegreesInvesting/r2dii.analysis: Measure Climate Scenario Alignment of Corporate Loans

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)

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.

``` {r use-r2dii} library(r2dii.data) library(r2dii.match) library(r2dii.analysis)

To plot your results, you may also load the package 
[r2dii.plot](https://2degreesinvesting.github.io/r2dii.plot).

``` {r use-r2dii.plot}
library(r2dii.plot)

We also recommend packages in the tidyverse; they are optional but useful.

``` {r use-tidyverse} library(tidyverse)

## Match your loanbook to climate-related asset-level data

See [r2dii.match](https://2degreesinvesting.github.io/r2dii.match) for a more 
complete description of this process. 

```r
# Use these datasets to practice but eventually you should use your own data.
# The optional syntax `package::data` is to clarify where the data comes from.
loanbook <- r2dii.data::loanbook_demo
abcd <- r2dii.data::abcd_demo

matched <- match_name(loanbook, abcd) %>% prioritize()

matched

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(
    abcd = abcd,
    scenario = scenario,
    region_isos = regions
  )

market_share_targets_portfolio

Or at the company level:

market_share_targets_company <- matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario,
    region_isos = regions,
    # Output results at company-level.
    by_company = TRUE 
  )

market_share_targets_company

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(abcd = abcd, co2_intensity_scenario = co2, region_isos = regions) %>% 
  filter(sector == "cement", year >= 2020)

sda_targets

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)

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)

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.

data <- filter(sda_targets, sector == "cement", region == "global")
qplot_emission_intensity(data)

2DegreesInvesting/r2dii.analysis documentation built on May 18, 2022, 2:48 a.m.