This script is the implementation of the method described in Rau et al. (2024). It uses carbon loss values in the project area and in the counterfactual scenario of a REDD+ project to simulate yearly values of:

1. Additionality (Mg CO2e)
2. Anticipated release (Mg CO2e)
3. Equivalent permanence (EP, between 0 and 1)
4. Reversal risks of (between 0 and 1)

The carbon loss values can be either taken from observed time series, randomly drawn from a distribution fitted to observed values, or randomly drawn from exponential distributions for a theoretical project.

Overview

1. SimulatePermanence() performs the simulation; its output is provided to the "outlist" argument of the SaveStandard() function
2. SaveStandard() performs basic summary and saves it to an RDS object; its output is provided to the "summary_out" argument of the PlotStandard() function
3. PlotStandard() generates and saves basic time series plots

Explanation of the SimulatePermanence() function

The core function that performs the simulation is SimulatePermanence(), which takes the following arguments:

1. type: character, "theo" for theoretical projects, "real" for real-life projects

2. mean_drawdown: numerical vector, mean drawdown rate(s) for theoretical project(s) Its inverse will be used as the lambda parameter of an exponential distribution When aggregate_type is NULL and the length of this argument > 1, it initialises a custom theoretical aggregated project

3. sites: character vector, name(s) for the real-life project(s) The function will look for a data frame of carbon flux time series at the path "/project_input_data/sites.csv" When aggregate_type is NULL and the length of this argument > 1, it initialises a custom real-life aggregated project

4. aggregate_type: character, specifying default settings for theoretical/real-life aggregated projects This argument overrides both mean_drawdown and sites when not NULL: "A": theoretical, mean_drawdown = c(1.1, 1.1, 1.1, 5) "B": theoretical, mean_drawdown = c(1.1, 1.1, 5, 5) "C": theoretical, mean_drawdown = c(1.1, 5, 5, 5) "three": real-life, sites = c("Gola_country", "CIF_Alto_Mayo", "VCS_1396") "four": real-life, sites = c("Gola_country", "CIF_Alto_Mayo", "VCS_1396", "VCS_934")

5. verbose: boolean, whether to print basic output at each timestep (default: FALSE)

6. runtime: boolean, whether to print (default: TRUE)

7. omega: numeric, threshold of acceptable reversal risk (default: 0.05)

8. n_rep: numeric, number of repetitions (default: 100)

9. H: numeric, project duration (years) (default: 50)

10. D: numeric, discount rate (default: 0.03)

11. warmup: numeric, warm-up period (years) (default: 5)

12. postproject_ratio: numeric, ratio of post-project release compared to during-project additionality accumulation rate (default: 2)

13. scc_df: numeric data frame, containing two columns, year and value (default: scc, a data frame which should be loaded and prepared)

Required input data

The dataset that one needs to prepare to run a simulation for a specific real-life project is the yearly project/counterfactual carbon fluxes in the project. It should be saved as a csv file in the "project_input_data" directory; the name of the file will be the "sites" variable. The file must contain the following columns:

1. year: numeric
2. var: character, either "project", "counterfactual" or "additionality"
3. val: numeric, total carbon flux from the last year to this year (Mg CO2e)
4. n_sim: numeric, index of repetition
5. started: boolean, whether the year is larger than project start (t0)

Other scripts

extend_scc.R and create_input_csv.R are maintenance scripts used to prepare input data. ViewSnapshot.r is a function that selects one particular repetition and view its time series, but is not currently used for anything.