Sus Fin Paper 2 Proposal and Initial Writing

Critical Mineral Market Paper Proposal

Research Question

How has the growth of Chinese electric vehicle (EV) exports influenced global energy security, particularly in relation to the production and supply of critical minerals like copper and nickel, and how should the industry adapt to meet rising global demand?

Introductory Data Exploration

Graphs

A graph showing projected minerals neede in EV, in percentage

A graph showing projected minerals needed in EV, in KT

As shown by graphs taken from the IEA official website, we can clearly see the projected future increase in demand for these critical minerals. The percentage of copper and nickel demanded in EV production have remained as the major mineral inputs for EVs. For that reason, it would be very valuable to look into questions related to these two particular minerals.

Introductory Data Tidying and Exploration (TBF)

library(readxl)
library(tidyr)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tibble)

# Load the dataset
minerals_data <- read_excel("CM_Data_Explorer.xlsx", sheet = "1 Total demand for key minerals")

New names:
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`
• `` -> `...14`
• `` -> `...15`
• `` -> `...16`
• `` -> `...17`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`

# Skip the first rows and set the second row as header using the read_excel function
minerals_data <- read_excel("CM_Data_Explorer.xlsx", sheet = "1 Total demand for key minerals",
                            skip = 4, col_names = TRUE)

New names:
• `` -> `...1`
• `` -> `...3`
• `2025` -> `2025...4`
• `2030` -> `2030...5`
• `2035` -> `2035...6`
• `2040` -> `2040...7`
• `2045` -> `2045...8`
• `2050` -> `2050...9`
• `` -> `...10`
• `2025` -> `2025...11`
• `2030` -> `2030...12`
• `2035` -> `2035...13`
• `2040` -> `2040...14`
• `2045` -> `2045...15`
• `2050` -> `2050...16`
• `` -> `...17`
• `2025` -> `2025...18`
• `2030` -> `2030...19`
• `2035` -> `2035...20`
• `2040` -> `2040...21`
• `2045` -> `2045...22`
• `2050` -> `2050...23`

# If the dataset has merged cells, we may need to fill down or fill across the values
minerals_data <- minerals_data %>%
  fill(names(minerals_data), .direction = "downup") 
 # fill(names(minerals_data), .direction = "left")

# Convert it to a tibble for a clean view
minerals_data_tibble <- as_tibble(minerals_data)

# Print the tibble to see
minerals_data_tibble

# A tibble: 55 × 23
   ...1      `2022` ...3  `2025...4` `2030...5` `2035...6` `2040...7` `2045...8`
   <chr>      <dbl> <lgl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
 1 Copper   6.82e+2 NA     779.       907.       925.       959.         1.12e+3
 2 Copper   6.82e+2 NA     779.       907.       925.       959.         1.12e+3
 3 Solar PV 6.82e+2 NA     779.       907.       925.       959.         1.12e+3
 4 Wind     3.94e+2 NA     428.       646.       595.       532.         6.39e+2
 5 Other l… 8.50e+1 NA      69.0      108.        86.9       84.7        1.10e+2
 6 Electri… 3.73e+2 NA     580.      1044.      1219.      1421.         1.46e+3
 7 Grid ba… 2.04e+1 NA      38.4       82.7      154.       225.         2.38e+2
 8 Electri… 4.18e+3 NA    4579.      6510.      6511.      6582.         7.11e+3
 9 Hydroge… 3.20e-3 NA       0.00388    0.00676    0.00653    0.00699    8.90e-3
10 Total c… 5.74e+3 NA    6473.      9298.      9491.      9805.         1.07e+4
# ℹ 45 more rows
# ℹ 15 more variables: `2050...9` <dbl>, ...10 <lgl>, `2025...11` <dbl>,
#   `2030...12` <dbl>, `2035...13` <dbl>, `2040...14` <dbl>, `2045...15` <dbl>,
#   `2050...16` <dbl>, ...17 <lgl>, `2025...18` <dbl>, `2030...19` <dbl>,
#   `2035...20` <dbl>, `2040...21` <dbl>, `2045...22` <dbl>, `2050...23` <dbl>

Research Question Prompt

Foreign Policy/Security: Considering China’s growing dominance in EV exports and the associated demand for critical minerals, evaluate the geopolitical and energy security implications for countries increasingly reliant on these imports. Analyze the strategies that nations could adopt to mitigate risks from potential supply disruptions or market manipulations by dominant exporting countries, especially in the context of the rising global demand for EVs. Examine how this dynamic influences international relations and the strategic positioning of nations in the global energy transition.

Research Plan Outline

Executive Summary:

China’s expansion in the global EV market is reshaping energy security dynamics.

Key Takeaways:

Chinese EV exports are driving a surge in global demand for critical minerals. Energy security now hinges on diversified, stable mineral supply chains. Strategic international partnerships are crucial to address potential supply monopolies.

Introduction:

Overview of China’s rise in the EV market and the subsequent global impact.

Body Paragraph 1:

China’s dominance in EV exports and the global market response. Visualization: Graph of Chinese EV export growth over the past decade.

Body Paragraph 2:

The critical role of minerals like cobalt and lithium in EV production. Visualization: Table showing the forecasted demand for these minerals.

Body Paragraph 3:

International efforts to secure stable mineral supplies and China’s strategic moves. Visualization: Map highlighting key areas of mineral extraction and trade routes.

Conclusion:

Summarize the implications of China’s EV market growth for global energy security and potential strategies for mitigating risks.