Final Project

Global Climate Fund Allocation: An Analysis of Geographical Disparities, Economic Ratios, and Environmental Impacts

Motivation

As climate-related challenges disproportionately affect the world’s most vulnerable populations, ensuring that GCF allocations are both effective and equitable is paramount. Evidence shows that targeted financial support can significantly enhance the adaptive capacities of these communities, mitigate the impacts of climate change, and promote sustainable development practices. However, concerns persist regarding the allocation mechanisms, which sometimes may not align perfectly with the regions most in need. Studies such as those by the UNFCCC have highlighted the importance of aligning funding with specific vulnerabilities and capacities, suggesting that a more strategic distribution could maximize both environmental and socio-economic benefits. Therefore, evaluating these aspects of GCF distributions is essential to optimize the global response to climate change and ensure that no vulnerable group is left behind in the shift towards a resilient, low-carbon future.

questions to answer

how funds are strategically allocated based on the climate risks and economic capabilities faced by each country; whether the allocation mechanisms truly account for the urgency of reducing greenhouse gas emissions and adapting to climate change; and whether the funds are genuinely supporting the nations most in need. These charts offer a multidimensional perspective that showcases the complexities and challenges inherent in the distribution of the Global Climate Fund.

options(repos = c(CRAN = "https://cran.rstudio.com/"))

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(sf))
#| include: false
#| message: false
#| warning: false

library(tidyverse)
library(readxl)
library(sf)
library(leaflet)
library(plotly)

fund <- read_excel("Green Climate Fund.xlsx")
world <- read_sf("custom.geo.json")


size_colors <- c("#fffcd3", "#fdde92", "#fea26c", "#e94e54", "gray")
size_levels <- c("Micro", "Small", "Medium", "Large", "NA")

new <- fund %>%
  filter(!str_detect(Countries, ",")) %>%
  rename(region = Countries, size = `Project Size`) %>%
  group_by(region, size) %>%
  filter(row_number() == 1) %>%
  select(region, size) %>%
  group_by(region) %>%
  filter(row_number() == 1) %>%
  mutate_at("size", ~factor(., levels = size_levels[1:4], ordered = T))


pal <- colorFactor(size_colors[1:4], levels = size_levels[1:4])

# 1
world %>%
  left_join(new %>% rename(name = region)) %>%
  leaflet() %>%
  setView(0,0,zoom = 1.5) %>%
  addPolygons(label = ~name, weight = 1,
              color = "white", fillColor = ~pal(size), 
              fillOpacity = 1) %>%
  addLegend(color = size_colors, title = "Project Size",
            opacity = 1, labels = size_levels,
            position = "bottomright")

Joining with `by = join_by(name)`

The map provides a compelling visual representation of the uneven distribution of climate finance funds across the globe. It categorically distinguishes regions by the scale of the funded projects, ranging from micro to large, yet numerous areas are marked as ‘NA’, indicating a lack of data or absence of funding. Notably, substantial investments are concentrated in select parts of Africa, Latin America, and Asia, suggesting a targeted approach to funding in these regions. However, the absence of specified project sizes in most developed regions, such as Europe and North America, could imply a disparity in the allocation process or potentially different funding mechanisms at play. This geographic distribution underscores a broader issue within global climate finance efforts: the need for a more equitable spread of resources that aligns with the pressing and diverse needs of regions facing the multifaceted impacts of climate change.

2

df <- data.frame(indicator = c("Adaptation", "Mitigation", "Dual benefits"),
                `2019` = c(42L, 282L, 14L),
                `2020` = c(55L, 261L, 16L),
                `2021` = c(54L, 453L, 42L),
                `2022` = c(70L, 619L, 41L))

rownames(df) <- c("Adaptation", "Mitigation", "Dual benefits")
new <- reshape2::melt(df, id.vars = 1, variable.name = "year")
new$year <- as.numeric(gsub("X", "", new$year))

plot_ly(data = new, x = ~year, y = ~value,type = "bar" ,color = ~indicator) %>%
  layout(title = "Global Public Climate Finance by Adaption/Mitigation")

The bar chart presents the allocation of global public climate finance over four consecutive years (2019-2022), categorized into three types of funding: Adaptation, Mitigation, and Dual benefits—which indicates funding that serves both adaptation and mitigation purposes.

The data indicates a substantial increase in funding dedicated to mitigation over the years, with a peak in 2022. This suggests a significant emphasis on efforts to reduce greenhouse gas emissions and slow the rate of global warming. In contrast, adaptation funding, which supports projects aiming to adjust to the effects of climate change, has remained relatively low and consistent throughout the period. The dual benefits category has seen a modest increase, but it remains a small portion of the overall financing.

The growing magnitude of mitigation funding reflects a global recognition of the urgency to address the root causes of climate change. This focus on mitigation might be due to its long-term benefits and the potential for scalable impact. However, the relatively static funding for adaptation raises concerns about whether enough is being done to protect vulnerable communities and ecosystems that are already experiencing the adverse effects of climate change. The consistent underrepresentation of adaptation and dual-benefit initiatives could be a signal that more balanced funding is needed to not only prevent future climate change but also to manage its current impacts.

3

Ratio

library(readxl)
library(plotly)
library(tidyverse)
imf <- read_excel("imf-dm-export-20240408.xls")
names(imf)[1] <- "country"
odl <- read_excel("ODL-Export-countries-1712604112761.xlsx")
odl %>%
  rename(country = `Country Name`) %>%
  inner_join(imf %>% select(country, `2023`)) %>%
  mutate_at("2023", ~as.numeric(str_replace_all(., "no data", ""))) %>%
  mutate(ratio = `FA Financing $`/`2023`) %>%
  plot_ly(y = ~country, x = ~ratio, type = "bar",
          hovertemplate = ~paste0("Country:", country, "\n",
                                  "FA Financing($):", `FA Financing $`, "\n",
                                  "Per GDP($):", `2023`, "\n",
                                  "Ratio:", ratio)) %>%
  layout(yaxis = list(categoryorder = "total ascending"),
         title = "the ratio of GCF funding to GDP per capital  for each country")

The bar chart illustrates the ratio of GCF funding to GDP per capita for each country, with Ethiopia at the top, suggesting it receives the highest proportion of GCF funds relative to its economic size.

In general, a high ratio of GCF funding to GDP per capita indicates a targeted strategy towards lower-income countries with potentially higher vulnerability to climate change impacts. Ethiopia, leading the chart, could exemplify a strategic focus where GCF allocations are potentially providing significant resources for climate adaptation and mitigation relative to the country’s economic capacity. This suggests an approach that prioritizes countries with fewer resources to address climate change independently, which could be a marker of an equitable distribution strategy.

4

CO2 <- read_excel("EDGARv8.0_FT2022_GHG_booklet_2023_fossilCO2_only.xlsx", 
                  sheet = "fossil_CO2_totals_by_country")%>%
  select(Country, `2022`)

funds <- read_excel("CFU-Website-MASTER-February-2024.xlsx", sheet = "Pledges")
combined_data <- funds %>%
  select(Country, `Country Income Level`, `Deposited (USD million current)`) %>%
  group_by(Country, `Country Income Level`) %>%
  filter(row_number() == 1) %>%
  left_join(CO2) %>%
  rename(value = `2022`) %>%
  mutate_at("Country Income Level", ~factor(., levels = c("Not applicable", "Lower middle income", "Upper middle income", "High income"), ordered = T))

plot_ly(data = combined_data, y = ~value, 
        x = ~`Deposited (USD million current)`, 
        mode = "markers",
        type = "scatter",
        marker = list(size = ~value/20,opacity = 0.4), 
        color = ~`Country Income Level`,
        colors = c("skyblue","green", "red"),
        hovertemplate = ~paste0("Country:", Country, "\n","Total CO2 Fossil Fuel Emission:", round(value), 
                                "\n", "Total Climate Fund Deposited (USD million current):", 
                                `Deposited (USD million current)`)) %>%
  layout(title = "the relationship between the total amount of climate fund deposited (in current USD million) and the total CO2 emissions from fossil fuel use",
         legend = list(title = list(text = "Income Group")),
       xaxis = list(title = "Total Climate Fund Deposited (USD million current)",type = "log"),
       yaxis = list(title = "Total CO2 Fossil Fuel Emission", type = "log"))

The scatter plot illustrates the relationship between the total amount of climate fund deposited (in current USD million) and the total CO2 emissions from fossil fuel use, with data points categorized by the income group of countries: high, upper-middle, and lower-middle income.

A clear trend is not immediately apparent from the plot, suggesting that the relationship between the climate fund received and CO2 emissions is not linear and may be influenced by multiple factors. However, larger bubbles, which represent higher CO2 emissions, seem more prevalent among the high-income group, potentially indicating that countries with higher emissions are receiving more climate funding. This could be due to their greater financial contribution to the funds or a recognition of their higher emissions and capacity to implement large-scale mitigation projects.

This chart could imply that while there is a recognition of responsibility from higher-income and higher-emission countries, lower-middle-income countries, which often are more vulnerable to climate change impacts, appear to receive less funding relative to their potential needs for adaptation and mitigation. This might raise questions about whether the allocation of climate funds is proportionate to the countries’ contributions to global emissions or their need for adaptation resources.

Conclusion

Integrating the content from the four provided charts, a pronounced disparity in the distribution of the Global Climate Fund across different nations is readily discernible. Initially, the map reveals a non-uniform geographical dispersion of funds, varying according to the size of the projects, with certain regions lacking clear data on allocations. Secondly, an analysis of the ratio of GCF funds to GDP per capita demonstrates that lower-income countries, such as Ethiopia, receive a relatively high proportion of climate funds relative to their economic scale. Moreover, the increase in climate funds is closely linked to efforts to reduce greenhouse gas emissions, particularly evident within high-income nations. Lastly, considering the relationship between the total amount of climate funds and fossil fuel emissions, the data suggests that although high-income countries have larger emissions, they also correspondingly receive more climate funding.