USAID Memo- Critical Minerals

Setup:

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

The downloaded binary packages are in
    /var/folders/yd/c2zm5hrx3z9f72991hsqsbqh0000gn/T//RtmpuEyK7e/downloaded_packages

install.packages("here")

The downloaded binary packages are in
    /var/folders/yd/c2zm5hrx3z9f72991hsqsbqh0000gn/T//RtmpuEyK7e/downloaded_packages

library(here)
library(tidyverse)
library(readxl)
library(tidyverse)
library(writexl)
library(esquisse)

path_to_sheet <- here("downloads", "CM_Data_Explorer.xlsx")
read_EV_sheet <- partial(
  .f = read_excel,
  path = path_to_sheet,
  sheet = "2.3 EV",
  col_names = FALSE
)
sheet_header_EV <- read_EV_sheet(range = "A4:W5")
sheet_header_EV_processed <- sheet_header_EV |> 
  t() |>
  as_tibble() |>
  rename(scenario = V1, year = V2) |>
  fill(scenario) |>
  replace_na(list(scenario = "Current Year"))
tech_scenario <- read_EV_sheet(range = "A7") |> 
  pull()
tech_info <- read_EV_sheet(range = "A8:W19")
tech_info_col_names <- names(tech_info)

EV_sheet_headers_and_col_names <- sheet_header_EV_processed |> 
  add_column(tech_info_col_names = tech_info_col_names)

tech_info_long <- tech_info |> 
  rename(indicator = `...1`) |> 
  pivot_longer(cols = -indicator,
               names_to = "tech_info_col_names") |> 
  add_column(tech_scenario)

EV_combined_data <- tech_info_long |> 
  left_join(EV_sheet_headers_and_col_names, by = join_by(tech_info_col_names)) |> 
  filter(!is.na(year)) |> 
  mutate(year = as.integer(year)) |> 
  select(tech_scenario, indicator, scenario, year, value)

tech_scenario_range <- "A7"
tech_info_range <- "A8:W19"

read_iea_EV_table <-
  function(tech_scenario_range, tech_info_range) {
    tech_scenario <-
      read_EV_sheet(range = tech_scenario_range) |>
      pull()
    
    tech_info <- read_EV_sheet(range = tech_info_range)
    
    tech_info_col_names <- names(tech_info)
    
    tech_info_long <- tech_info |>
      rename(indicator = `...1`) |>
      pivot_longer(cols = -indicator,
                   names_to = "tech_info_col_names") |>
      add_column(tech_scenario)
    
    EV_combined_data <- tech_info_long |>
      left_join(EV_sheet_headers_and_col_names, by = join_by(tech_info_col_names)) |>
      filter(!is.na(year)) |>
      # case_when is supercharged if else
      mutate(year = as.integer(year)) |>
      select(tech_scenario, indicator, scenario, year, value)
    
    EV_combined_data
  }

# reading in charts
nickle_supply_table <- read_iea_EV_table(
  tech_scenario_range = "A7",
  tech_info_range = "A8:W19"
)
silicon_table <- read_iea_EV_table(
  tech_scenario_range = "A22",
  tech_info_range = "A23:W34"
)
batteries_table <- read_iea_EV_table(
  tech_scenario_range = "A37",
  tech_info_range = "A38:W49"
)
low_battery_table <- read_iea_EV_table(
  tech_scenario_range = "A52",
  tech_info_range = "A53:W64"
)
limited_battery_table <- read_iea_EV_table(
  tech_scenario_range = "A67",
  tech_info_range = "A68:W79"
)
base_case_table <- read_iea_EV_table(
  tech_scenario_range = "A82",
  tech_info_range = "A83:W94"
)
# binding rows 
final_iea_EV_table <- nickle_supply_table |> 
  bind_rows(silicon_table) |> 
  bind_rows(batteries_table) |>
  bind_rows(low_battery_table) |>
  bind_rows(limited_battery_table) |>
  bind_rows(base_case_table)

# Don't print final_iea_EV_table

INTERNATIONAL DEVELOPMENT AGENCY QUESTION:

Imagine that you are an economic adviser within USAID’s Office of the Chief Economist. The Administrator is interested in understanding how USAID support in emerging markets can adapt to global market transitions towards electric vehicles (EVs). Her team has asked you to draft a memo based on the IEA Critical Mineral Report identifying trends in critical mineral markets that would inform USAID’s prioritization of mineral sectors using data analysis and visualizations based on the IEA’s Critical Mineral Report. The Administrator’s team has informed you that she is currently considering requests from the US Ambassador to Madagascar and Zambia on USAID-supported mining compliance work and value chain development. There is a limited number of non-committed funds in this fiscal year, and she is curious about what countries to prioritize.

INSIGHTS FOR USAID FROM THE IEA REPORT

EXECUTIVE SUMMARY:

• Increased EV production will mean elevated demand for copper, graphite, Nickel.

• Zambia and Madagascar’s mineral deposits mean they could stand to capture significant economic benefits if they are prepared.

• If USAID resources are limited, assistance oriented toward developing graphite projects in Madagascar may be more prudent according to global policy and technology trends.

INTRODUCTION:

The global transition to EVs has prompted an unprecedented level of demand for critical minerals. These factors provide a tremendous opportunity for many resource-rich emerging markets to advance economic growth. The increased demand means that USAID can achieve a significant impact by helping resource-rich emerging markets; 1) capture the maximum value of their natural resources and 2) engage in sustainable mining practices. Investments in refinery capacity development, which are extremely scarce in emerging markets, can go a long way to make sure that wealth stays within emerging markets. Fostering compliance with international sustainability standards can ensure that the mining industry does not harm human rights or the environment. Recently collected data by the IEA can help inform USAID’s country prioritization strategy when deciding where to prioritize this assistance. Zambia and Madagascar are both resource-rich nations that could benefit from USAID assistance, but the prioritization of these countries should be informed by global market trends to ensure maximum impact.

ANALYSIS:

The first key insight we can draw from IEA data is that graphite, copper, and nickel will continue to have increased demand due to increased EV production. As Figure 1 demonstrates, graphite, copper, and nickel will be the three most in-demand minerals compared to other EV parts across each of these scenarios. These trends bode well for both Zambia and Madagascar which are particularly resource rich in copper and graphite respectively. According to the US Geological Survey (USGS), Zambia is among the world’s largest copper producers. Recent USGS data shows that Madagascar is the world’s second-largest Graphite producer. Yet, neither country has significant refinery capacities nor high levels of compliance with international labour standards. Supporting either of these capacities could result in significant economic development potential for each country.

FIGURE 1:

ggplot(final_iea_EV_table) +
  aes(x = indicator, y = value, colour = tech_scenario) +
  geom_col(fill = "#112446") +
  scale_color_hue(direction = 1) +
  labs(
    x = "Critical Mineral",
    y = "Thousands of Tons (kt)",
    title = "EV Vehicle Mineral Demand",
    subtitle = "Copper and Graphite Have the Highest Demand of the Minerals",
    caption = "Data from IEA | Insights by Niki Linganur",  # Updated author name
    color = "Possible Demand Scenarios"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = .8))

If we analyse further and compare the demand scenarios between copper and graphite across technology innovation scenarios, we see both resources will have elevated demand across a range of scenarios (Figure 4). Data from the IEA projects the fluctuation in demand for graphite (Figure 2) and copper (Figure 3) across a range of likely technology developments including constrained nickel supply, faster uptake of solid-state batteries, limited battery size production, lower battery sizes, STEPS-Base cases, and wider use of silicon-rich anodes. Both resources will be in particularly high demand in scenarios where there is innovation in battery and anode technology. Given there is a larger trend towards advancement in these areas, this further reinforces the potential of investments in these mineral supply chains. This means that investment in value capture capacity such as refineries, as the report flags many countries are profiting from, could provide significant development opportunities in both Zambia and Madagascar.

FIGURE 2:

filtered_graphite <- final_iea_EV_table %>%
  filter( indicator == "Graphite")

ggplot(filtered_graphite, aes(y = tech_scenario, x = value)) +
  geom_col(fill= "grey") +  
  labs(title = "Graphite Demand by Scenario",
       subtitle = "Graphite Demand Will Lessen if Batteries Get Smaller",
       caption = "Data from IEA | Insights by Niki Linganur",
       y = "Scenario",
       x = "Graphite Demand (kt)") +
  theme_minimal()

FIGURE 3:

filtered_copper <- final_iea_EV_table %>%
  filter( indicator == "Copper")

ggplot(filtered_copper, aes(y = tech_scenario, x = value)) +
  geom_col(fill= "#B87333") +  
  labs(title = "Copper Demand by Scenario",
       subtitle = "Copper Demand is Highest with Constrained Nickle Supply",
       caption = "Data from IEA | Insights by Niki Linganur",
       y = "Scenario",
       x = "Copper Demand (kt)") +
  theme_minimal()

FIGURE 4:

filtered_copper_graphite <- final_iea_EV_table %>%
  filter(indicator == "Copper" | indicator == "Graphite")

ggplot(filtered_copper_graphite) +
  aes(x = value, y = tech_scenario, colour = indicator) +
  geom_boxplot(fill = "#112446") +
  scale_color_manual(
    values = c(Copper = "#B87333",
    Graphite = "#808080")
  ) +
  labs(
    x = "Graphite and Copper Demand (kt)",
    y = "Scenarios",
    title = "Comparison of Copper and Graphite Demand",
    subtitle = "Battery Size Reduction or Constrained Nickel Supply will Drive up Demand",
    caption = "Data from IEA | Insights by Niki Linganur",
    color = "Mineral"
  ) +
  theme_minimal()+
theme(
    plot.title = element_text(hjust = 0),  # Align title text to the left
    plot.title.position = "plot"  # Position title relative to the plot area
  )

Yet, the IEA report does seem that likely technology trends will particularly reinforce graphite demand shortly. The report highlights that Graphite and rare earth elements are set to gain increasing traction in the critical minerals’ discussion. Anode chemistries in batteries are seeing a growing adoption of silicon-doped graphite. This is largely because the energy density and charging speeds of graphite anodes have been significantly boosted by doping the graphite anode with silicon. Natural graphite also could come with the side benefit of easier emissions compliance. An increased supply of natural graphite will also mean lower emissions given synthetic graphite anodes have higher emissions than natural graphite anodes. Yet this demand could decrease in the eventuality that Solid-state batteries proliferate. Solid-state batteries go even further by switching the traditional graphite anodes to lithium metal anodes or pure silicon, which could significantly enhance energy density and thermal safety.

CONCLUSION:

Overall, according to the IEA data, USAID should prioritize investments in critical mineral development related to high-demand minerals like copper, graphite, and nickel. When it comes to the cases of Zambia and Madagascar, both countries have significant potential for economic development if these sectors are well prepared. As the report flags, countries can benefit from investments in refinery capacities and sustainability standards which derrick projects from foreign investors. Yet, if USAID resources are scarce, market trends in graphite could pose significant opportunities for Madagascar which is well positioned by its large production. This is largely due to the likely continued trend of graphite used in anode innovation.