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s3.R
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# load packages
library(tidyverse)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(cowplot)
library(magrittr)
library(RColorBrewer)
library(weights)
library(GGally)
library(patchwork)
# set theme to classic and define colour palette
theme_set(theme_classic())
my_colors = colorRampPalette(brewer.pal(9, "BuGn"))(10)[c(4,6,8,10)]
# statistical assessments
tw_2020_df = read.csv(file.path("csvs", "tw_2020_avoided_amounts.csv"))
tw_2023_df = read.csv(file.path("csvs", "tw_2023_avoided_amounts.csv"))
tw_2024_df = read.csv(file.path("csvs", "tw_2024_avoided_amounts.csv"))
ag_2022_df = read.csv(file.path("csvs", "ag_2022_avoided_amounts.csv"))
ag_2024_df = read.csv(file.path("csvs", "ag_2024_avoided_amounts.csv")) %>%
select(ID = vcs_id, Avd_def = DD_ha) %>%
mutate(ID = as.numeric(str_replace(ID, "PL", ""))) %>%
mutate(Avd_def = -Avd_def) # reverse sign for avoided deforestation
# pact assessments
pact_v2_df = read.csv(file.path("csvs", "pact_2025_avoided_amounts.csv")) %>%
filter(id != "1325") %>%
rename(
Start = start_year,
End = end_year,
ID = id
)
# certified assessments
vcs_df = as.data.frame(read.csv(file.path("csvs", "certified_avoided_amounts.csv")))
# align certified assessment data format with statistical data
vcs_df$total.avoided.def[which(vcs_df$total.avoided.def == "#VALUE!" | vcs_df$total.avoided.def == "")] = NA
vcs_df$total.additionality[which(vcs_df$total.additionality == "#VALUE!" | vcs_df$total.additionality == "")] = NA
vcs_df$raw.additionality[which(vcs_df$raw.additionality == "#VALUE!" | vcs_df$raw.additionality == "")] = NA
vcs_df$total.avoided.def = as.numeric(vcs_df$total.avoided.def)
vcs_df$total.additionality = as.numeric(vcs_df$total.additionality)
vcs_df$raw.additionality = as.numeric(vcs_df$raw.additionality)
vcs_df$def.emissions.ONLY = as.numeric(vcs_df$def.emissions.ONLY)
vcs_df$Start = as.numeric(vcs_df$Start)
vcs_df$End = as.numeric(vcs_df$End)
# select and rename columns for certified assessments
vcs_df = vcs_df %>%
select(c("ID", "Start", "End", "total.avoided.def")) %>%
rename(Avd_def = total.avoided.def) %>%
mutate(source = "VERRA",
label = "VERRA")
# align statistical assessments for tw_2020
tw_2020_df = tw_2020_df %>%
select(c("ID", "start", "end", "avd_def")) %>%
rename(Start = start, End = end, Avd_def = avd_def) %>%
mutate(source = "TW_2020",
label = "west 20")
# align statistical assessments for tw_2023
tw_2023_df = tw_2023_df %>%
select(c("ID", "Start", "End", "Avoided_def")) %>%
rename(Avd_def = Avoided_def) %>%
mutate(source = "TW_2023",
label = "west 23")
# align statistical assessments for tw_2024 (filtered for a specific project)
tw_2024_df = tw_2024_df %>%
select(ID, Start = year_start, End = year_end, Avd_def = def) %>%
filter(ID == 934) %>%
mutate(source = "TW_2023", # note: combined with tw_2023 for simplicity
label = "west 23")
# align statistical assessments for ag_2022
ag_2022_df$Start = 0
ag_2022_df$End = 5
ag_2022_df = ag_2022_df %>%
select(ID, Start, End, Avoided_def) %>%
rename(Avd_def = Avoided_def) %>%
mutate(source = "AG_2022",
label = "guizar-coutino 22")
# align statistical assessments for ag_2024
ag_2024_df = ag_2024_df %>%
mutate(Start = 0, End = 5) %>%
select(c("ID", "Start", "End", "Avd_def")) %>%
mutate(source = "AG_2024",
label = "guizar-coutino 24")
# align pact assessments
pact_v2_df = pact_v2_df %>%
select(c("ID", "Start", "End", "avoided_disturbance_ha")) %>%
rename(Avd_def = avoided_disturbance_ha) %>%
mutate(source = "PACTv2",
label = "pactv2")
# combine all datasets into one
combined_df = rbind(tw_2020_df, tw_2023_df, tw_2024_df, ag_2022_df, ag_2024_df, pact_v2_df, vcs_df) %>%
mutate(ID = as.factor(ID)) %>%
mutate(avd_def_yr = Avd_def / (End - Start))
# read project metadata and combine with assessments
all_projects_df = read.csv("csvs/project_metadata.csv") %>%
select(ID, method = Methodology, country = Country.Area)
combined_df = combined_df %>%
left_join(all_projects_df %>% mutate(ID = as.factor(ID))) %>%
mutate(
source = as.factor(source),
label = as.factor(label),
method = as.factor(method),
country = as.factor(country)
) %>%
mutate(counterfactual = ifelse(source == "VERRA", 0, 1))
# filter to only include unplanned deforestation methodologies
combined_df = combined_df %>%
filter(method %in% c("VM0006", "VM0007", "VM0009", "VM0015"))
# align the datasets for certified vs. statistical assessments
cf_vcs_comp_def_df = combined_df %>%
select(ID, avd_def_yr, source) %>%
group_by(ID) %>%
pivot_wider(names_from = source, values_from = avd_def_yr) %>%
filter(!is.na(VERRA)) %>%
pivot_longer(cols = c(-ID, -VERRA), names_to = "cf_source", values_to = "cf_avd") %>%
ungroup() %>%
filter(!is.na(cf_avd)) %>%
mutate(Aadj = cf_avd / VERRA)
cf_vcs_comp_def_df$cf_source = as.factor(cf_vcs_comp_def_df$cf_source)
# calculate summary statistics for each project
cf_vcs_avd_comp_mean_df = cf_vcs_comp_def_df %>%
filter(ID != 904) %>%
group_by(ID) %>%
summarise(
n = n(),
cf_avd_mean = mean(cf_avd),
cf_avd_min = min(cf_avd),
cf_avd_max = max(cf_avd),
cf_avd_sd = sd(cf_avd),
cf_avd_se = cf_avd_sd / sqrt(n),
cf_avd_ci = 1.96 * cf_avd_se,
cf_avd_ci_min = cf_avd_mean - cf_avd_ci,
cf_avd_ci_max = cf_avd_mean + cf_avd_ci,
cf_Aadj_mean = mean(Aadj),
cf_Aadj_min = min(Aadj),
cf_Aadj_max = max(Aadj),
cf_Aadj_sd = sd(Aadj),
cf_Aadj_se = cf_Aadj_sd / sqrt(n),
cf_Aadj_ci = 1.96 * cf_Aadj_se,
cf_Aadj_ci_min = cf_Aadj_mean - cf_Aadj_ci,
cf_Aadj_ci_max = cf_Aadj_mean + cf_Aadj_ci,
VERRA = VERRA[1]
)
# filter negative add ratio points for manual addition
neg_points_df = cf_vcs_avd_comp_mean_df %>%
filter(VERRA < 0) %>%
select(ID, cf_avd_mean, cf_Aadj_mean, VERRA, n) %>%
mutate(y_axis = 0,
cf_Aadj_mean = cf_Aadj_mean * -1)
# prepare data for plotting fig 2a (line data)
new_dat_df = data.frame(cf_avd_mean = seq(from = min(cf_vcs_avd_comp_mean_df$cf_avd_min),
to = max(cf_vcs_avd_comp_mean_df$cf_avd_max), by = 1)) %>%
mutate(VERRA = cf_avd_mean)
# plot s3a (scatter and line plot)
s3a_plot = cf_vcs_avd_comp_mean_df %>%
ggplot(aes(y = VERRA, x = cf_avd_mean)) +
geom_text_repel(aes(label=ID,color = as.factor(n)), size = 3, max.overlaps = 20)+
geom_line(data = new_dat_df, col = "black", linetype = 2, linewidth = 0.75)+
geom_vline(xintercept = 0, col = "black", linetype = 1, linewidth = 0.5)+
geom_hline(yintercept = 0, col = "black", linetype = 1, linewidth = 0.5)+
geom_point(size = 3, alpha = 0.8, aes(color = as.factor(n)))+
geom_errorbar(aes(xmin = cf_avd_ci_min,
xmax = cf_avd_ci_max, color = as.factor(n))) +
scale_x_continuous(limits = c(-1000, 3500)) +
scale_y_continuous(trans='log10', limits = c(50, 10000)) +
scale_color_manual(values = my_colors,
labels = c("1", "2","3","4")) +
ylab("Certified avoided deforestation (ha/year)") + xlab("Quasi-experimental assessment of avoided deforestation (ha/year)")+
theme(axis.line = element_blank(),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13),
legend.position="bottom",
legend.spacing = unit(0, "cm")
) +
geom_point(data = neg_points_df, aes(x = cf_avd_mean, y = y_axis, color = as.factor(n)), shape = 1,
size=3) + # manually add VERRA negative points
geom_text_repel(data = neg_points_df, aes(x = cf_avd_mean, y = y_axis, label = ID, color = as.factor(n)),
size = 3, max.overlaps = 20) + # add labels for neg points
guides(shape = guide_legend(ncol=2, bycol=TRUE, title = NULL,
override.aes = list(size = 4, col = "black")),
color = guide_legend(ncol=4, bycol=TRUE, title = "Number of studies",
override.aes = list(shape = 16, size = 3, alpha = 0.5)))
aadj_mean = mean(cf_vcs_avd_comp_mean_df$cf_Aadj_mean)
aadj_w_mean = weighted.mean(cf_vcs_avd_comp_mean_df$cf_Aadj_mean, cf_vcs_avd_comp_mean_df$VERRA)
s3b_plot = cf_vcs_avd_comp_mean_df %>%
ggplot(aes(x = VERRA, y = cf_Aadj_mean, label = ID)) +
geom_text_repel(aes(color = as.factor(n)), size = 3, max.overlaps = 20)+
geom_vline(xintercept = 0, col = "black", linetype = 1, linewidth = 0.5)+
geom_hline(yintercept = 0, col = "black", linetype = 1, linewidth = 0.5)+
geom_hline(yintercept = 1, col = "black", linetype = 2, linewidth = 0.75)+
geom_hline(yintercept = aadj_mean, linewidth = 0.75, col = 'grey')+
geom_hline(yintercept = aadj_w_mean, linewidth = 0.75, col = 'grey', linetype = 2)+
geom_point(size = 3, alpha = 0.8, aes(color = as.factor(n)))+
geom_errorbar(aes(ymin = cf_Aadj_ci_min, ymax = cf_Aadj_ci_max, color = as.factor(n))) +
scale_color_manual(values = my_colors,
labels = c("1", "2","3","4")) +
xlab("Certified avoided deforestation (ha/year)") + ylab("Additionality ratio")+
scale_x_continuous(trans='log10') +
theme(axis.line = element_blank(),
axis.text = element_text(size = 12),
axis.title = element_text(size = 13),
legend.position="bottom",
legend.spacing = unit(0, "cm")) +
shape = 1, size = 3) +
geom_text_repel(data = neg_points_df, aes(y = cf_Aadj_mean, x = 0, label = ID, color = as.factor(n)),
size = 3, max.overlaps = 20) + # add labels for neg points
guides(shape = guide_legend(ncol=2, bycol=TRUE, title = NULL,
override.aes = list(size = 4, col = "black")),
color = guide_legend(ncol=4, bycol=TRUE, title = "Number of studies",
override.aes = list(shape = 16, size = 3, alpha = 0.5)))
# save final figure to png
s3_plot = s3a_plot + s3b_plot +
plot_layout(ncol=1)+
plot_annotation(tag_levels='a') &
theme(plot.tag = element_text(size=25))
ggsave(s3_plot, filename=file.path("pngs",'s3_raw.png'),dpi=300,width=7,height=10)