Assessing Needs for Climate Finance from Developing Countries

Author

Dandan Gong

Code 
library(writexl)
library(countrycode)
library(tidyverse)
library(janitor) 
library(countrycode)
library(ggplot2)
library(hrbrthemes)
library(viridis)
library(readxl)
library(here)

Introduction

Financing is the center piece of climate negotiation for this year’s COP in Azerbaijan. Increasing support from developed countries to developing countries is critical for the implementation of the Paris Agreement, and substantiated data on the needs from developing countries is central to obtaining higher pledges from developed countries. This paper aims to contribute to the understanding of how much need has been expressed by developing countries of all shapes and sizes and explore some of the issues surrounding access to financing for those who are most vulnerable.

Methodology

Current climate financing tracking predominantly focuses on financing flows or utilizes top-down models for projections. This study provides a bottom-up approach by examining national documents from developing country Parties submitted as part of the COP process, detailing their needs for implementing the Convention and the Paris Agreement.

Non-Annex I Parties voluntarily assess financing, technological transfer, and capacity building needs. Out of 154 Non-Annex I countries, 64 have detailed their needs in Nationally Determined Contribution (NDC) documents, and additional data from 33 countries expressing financing needs in National Communication documents is included. This study presents both conditional (dependent on international support) and unconditional financing needs. Although not all countries have fully quantified costs, this study compiles available data up to the latest needs assessment round as of 2021, highlighting key insights.

Who Needs Financing?

97 non-Annex I countries have in aggregate indicated 14 trillion USD in financing requirements. Over half of this sum originates from China alone, at 8.5 trillion USD. India follows second, at 2.5 trillion USD, and South Africa has the third largest need at 1.5 trillion USD. These 3 countries alone represent over 86% of current expressed needs. The rest of the 94 countries have expressed 2 trillion USD in needs. This stunning discrepancy highlights the lack of available data, tools, and capacity for determining and costing needs that development agencies should help provide. Additionally, these figures have sparked controversy in international climate negotiations. Developed countries are more inclined to distribute aid to many poorer countries, rather than a few emerging markets, even though China and India houses over 45% of the population in non-Annex I countries.

Code 
all_hands_bloomberg <- read_csv("C:/Users/owner/Desktop/Sustainable Finance/R/climate_finance_needs.csv")
top_countries <- all_hands_bloomberg %>%
    group_by(country_name) %>%
  summarise(total = sum(total)) %>%
  arrange(desc(total)) %>%
  top_n(3)

other_countries <- all_hands_bloomberg %>%
  filter(!country_name %in% top_countries$country_name) %>%
  summarise(total = sum(total, na.rm = TRUE)) %>%
  mutate(country_name = "Others combined")

# Combine top countries and other countries data
combined_data <- bind_rows(top_countries, other_countries)

combined_data$country_name <- factor(
  combined_data$country_name,
  levels = c(top_countries$country_name, "Others combined")
)

chart1 <- combined_data |>
  ggplot(aes(x=country_name, y=total)) +
  geom_bar(stat="identity") +
  geom_col(fill = "green4", alpha=.9) +
  theme_minimal()+
  theme(legend.position = "",legend.title = element_blank())+
  scale_y_continuous(labels = scales::label_dollar(suffix = " BN")) +
  labs(
    x="",
    y="Expressed climate financing needs",
    title="97 developing countries expressed need for 14 trillion USD climate financing",
    subtitle="China, India, and South Africa expressed highest needs",
    caption="Data compiled from national reports submitted to the UNFCCC")+
    xlab("")
chart1

Who Are the Most Vulnerable?

We now take a closer look into 94 countries that together require 2 trillion USD. The financing needs of each country is adjusted for population to better compare across economies of different sizes. Two groups of countries reveal intriguing results. Small Island Developing States (SIDS) tend to require more financing as a reflection of higher costs of infrastructure project development on remote islands and their heightened vulnerability to climate change impacts. Least Developed Countries (LDC), as a group, show the highest vulnerability to climate change compared with other countries.

Code 
chart2 <- all_hands_bloomberg |> 
  filter(!(country_name %in% c("China", "India", "South Africa"))) |> 
  select(country_name, iso3c, gdp_bn_usd, gdp_per_capita_usd, total, need_percapita, nd_gain_vulnerability_score, population_mn, least_developed_countries_ldc, small_island_developing_states_sids, region_name, sub_region_name) |> 
  filter(total>0) |> 
  mutate(special_needs = case_when(
    small_island_developing_states_sids == 1 & is.na(least_developed_countries_ldc) ~ "Small Island",
    is.na(small_island_developing_states_sids) & least_developed_countries_ldc == 1 ~ "LDC",
    small_island_developing_states_sids == 1 & least_developed_countries_ldc == 1 ~ "Small Island LDC",
    TRUE ~"Other"  
    ))
  
custom_colors <- c(
  "Small Island LDC" = "#CC0000",  
  "Small Island" = "#FF6600",  
  "LDC" = "#FFCC00",
  "Other" = "#CCCCCC"
)

legend_order <- c("Small Island LDC", "Small Island", "LDC", "Other")

chart2$special_needs <- factor(chart2$special_needs, levels = legend_order)

graph2 <- chart2 |>
  ggplot(aes(x = nd_gain_vulnerability_score, y = need_percapita, color = special_needs)) +
  geom_point(size = 6, alpha = 0.7) +
  scale_color_manual(values = custom_colors) +  # Assign custom colors
  theme_minimal() +
  theme(legend.position = "bottom",legend.title = element_blank())+
  labs(
    x = "Climate vulnerability index",
    y = "Climate finance needs per capita",
    title = "Small islands countries need the highest finance support per capita",
    subtitle = "Per capita financing needs from 94 developing countries",
    caption = "Data compiled from national reports submitted to the UNFCCC, ND-GAIN Vulnerability Index, IMF WEO Database",
    color = ""
  ) +
  scale_y_continuous(labels = scales::label_dollar(suffix = ""))
graph2

Is Financing Accessible?

Timely and affordable access is at the heart of climate finance. We will focus further on SIDS as a case study of the accessibility of concessionary financing.

Relative to adaptation, financing for mitigation has been much greater and is also more successful in attracting private sector financing, particularly for renewable energy projects that are financially profitable. Financing for adaptation, on the other hand, relies heavily on concessionary financing and aid due to its focus on disaster recovery and on building resilience that will only realize avoided damages in the longer-term. Presently, a significant portion of concessionary climate financing comes from multilateral development mechanisms like the World Bank and OECD climate programs. Many SIDS have voiced that obtaining funding from the World Bank and UNDP, even after a disaster strikes, takes at least 12 months. Additionally, several SIDS do not meet the criteria for receiving concessionary financing. As illustrated in the following chart, highly vulnerable SIDS like Antigua and Barbuda are unable to access ODA. This underscores the mismatch between existing multilateral financial mechanism built for development and the urgent and distinct needs for addressing climate change.

Code 
chart3 <- all_hands_bloomberg |> 
  select(country_name, iso3c, gdp_bn_usd, gdp_per_capita_usd, total, 
         need_percapita, nd_gain_vulnerability_score, population_mn, 
         least_developed_countries_ldc, small_island_developing_states_sids, 
         oda_eligible_oecd_dac,
         region_name, sub_region_name, risk_of_distress) |> 
  filter(total>0) |> 
  mutate(oda = 
           case_when(
              oda_eligible_oecd_dac == 1 ~ "ODA eligible",
              TRUE ~"Not ODA eligible"  
            ))

custom_colors <- c(
  "ODA eligible" = "#0066cc",  
  "Not ODA eligible" = "#FF6600"
)

graph3 <- chart3 |>
  filter(small_island_developing_states_sids == 1) |>
  filter(!is.na(nd_gain_vulnerability_score)) |>
  mutate(country_name = fct_reorder(as.factor(country_name), nd_gain_vulnerability_score)) |>
  ggplot(aes(x = country_name, y = nd_gain_vulnerability_score, fill = oda)) +
  geom_col() +
  scale_fill_manual(values = custom_colors) +  # Use scale_fill_manual for fill colors
  theme_minimal() +
  theme(legend.position = "bottom", legend.title = element_blank()) +
  labs(
    x = "",
    y = "Climate vulnerability index",
    title = "Current financial mechanisms are designed for development, not for climate",
    subtitle = "Several highly vulnerable small island countries lack eligibility to ODA funding for urgent adaptation financing",
    caption = "ND-GAIN Vulnerability Index, OECD"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  guides(fill = guide_legend(title = ""))  # Show legend with custom title

# Display the graph
graph3

In conclusion, a thorough understanding of climate financing needs in developing countries is crucial for the effective mobilization of international financial assistance to aid developing countries in mitigating and adapting to climate change. Robust data, improved assessment methodologies, and transparency will help provide developing countries with better negotiation leverage and expedite the global effort to implement the Paris Agreement.

Reference