Comparison of Absolute Value Growth from 2022 to 2050
The chart illustrate a marked contrast between the two scenarios for Copper, with the “Net Zero Emissions by 2050 scenario” indicating a much higher demand by 2050 compared to the “Stated policies scenario.” This suggests that efforts to achieve net-zero emissions could significantly increase the demand for Copper, likely due to its essential role in renewable energy technologies and infrastructure.
library(tidyverse)
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library(ggplot2)# Load the datasetfinal_minerals_table_EV_modified <-read_csv("data/final_minerals_table_EV_modified.csv")
Rows: 1368 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): EV_tech_assumption, indicator, scenario, unit
dbl (2): year, value
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# Filter data for "Stated policies scenario" and "Net Zero Emissions by 2050 scenario"filtered_data <- final_minerals_table_EV_modified %>%filter(scenario %in%c("Stated policies scenario", "Net Zero Emissions by 2050 scenario")) %>%filter(year %in%c(2022, 2050))# Calculate the absolute value growth from 2022 to 2050 for each indicator within each scenariogrowth_data <- filtered_data %>%group_by(indicator, scenario) %>%summarise(growth =diff(value)) %>%ungroup() %>%# Remove indicators with 0 growthfilter(growth !=0)
Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
dplyr 1.1.0.
ℹ Please use `reframe()` instead.
ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
always returns an ungrouped data frame and adjust accordingly.
`summarise()` has grouped output by 'indicator', 'scenario'. You can override
using the `.groups` argument.
# Plotting the growthgrowth_plot <-ggplot(growth_data, aes(x = indicator, y = growth, fill = scenario)) +geom_bar(stat ="identity", position ="dodge") +theme_minimal() +labs(title ="Absolute Value Growth from 2022 to 2050",x ="Indicator",y ="Growth (kiloton)",fill ="Scenario") +theme(axis.text.x =element_text(angle =45, hjust =1)) +# Ensure y-axis starts from 0scale_y_continuous(limits =c(0, NA))print(growth_plot)
2”Demand Trends for Copper and Cobalt Over Time (STEPS - Base case)
The demand for Copper is projected to increase significantly over time.This increase in demand is likely driven by the expanding use of electric vehicles and possibly other technologies that require Copper for their manufacturing. Similarly, the demand for Cobalt is also projected from 2025 to 2050 under the same scenario. The average demand for Cobalt over these years is about 88.775 kilotons, also reflecting a significant increase over time.The increase in Cobalt demand can be attributed to its critical role in battery production for electric vehicles, among other uses.
# Load necessary librarieslibrary(ggplot2)library(dplyr)# Read the datasetdata <-read.csv("data/final_minerals_table_EV_modified.csv")# Filter out the "Current Year" scenario and limit to "STEPS - Base case"filtered_data <- data %>%filter(scenario !="Current Year", EV_tech_assumption =="STEPS - Base case",indicator %in%c("Copper", "Cobalt"))# Plot the trend of copper and cobalt demand over time, excluding "Current Year"# and limiting to "STEPS - Base case"ggplot(filtered_data, aes(x = year, y = value, color = indicator)) +geom_line() +theme_minimal() +labs(title ="Demand Trends for Copper and Cobalt Over Time (STEPS - Base case)",x ="Year",y ="Demand (kiloton)",color ="Mineral") +theme(plot.title =element_text(hjust =0.5),legend.position ="bottom")
3 Comparison of Copper and Cobalt Demand in 2025
These figures indicate that the demand for both Copper and Cobalt in 2025 varies significantly depending on the technological assumptions about electric vehicle (EV) technologies. Notably, scenarios that imply advancements in battery technology, such as the “Faster uptake of solid state batteries” and “Wider use of silicon-rich anodes,” tend to have higher demands for Cobalt compared to other scenarios. Similarly, the “Constrained nickel supply” scenario predicts the highest demand for Copper, suggesting that constraints on other materials could increase the reliance on Copper in EV technologies.
# Load necessary librarieslibrary(ggplot2)library(dplyr)# Read the datasetdata <-read.csv("data/final_minerals_table_EV_modified.csv")# Filter data to exclude "Limited battery size reduction" and "STEPS - Base case" for indicators "Copper" and "Cobalt"filtered_data <- data %>%filter(!EV_tech_assumption %in%c("Limited battery size reduction", "STEPS - Base case"), indicator %in%c("Copper", "Cobalt"))# Assuming you want to focus on a specific year, for example, 2025filtered_data_2025 <- filtered_data %>%filter(year ==2025)# Plot the filtered data for the year 2025ggplot(filtered_data_2025, aes(x=EV_tech_assumption, y=value, fill=indicator)) +geom_bar(stat="identity", position="dodge") +theme_minimal() +labs(title="Comparison of Copper and Cobalt Demand in 2025",x="Mineral",y="Demand (kiloton)",fill="Mineral")
4 Scenario Impact on Copper and Cobalt Demand
These results illustrate the significant variation in demand for Copper and Cobalt across different future scenarios, emphasizing the impact of technological advances and policy decisions on mineral demand. Notably, the “Limited battery size reduction” scenario leads to the highest demand for both Copper and Cobalt in 2040, indicating that technological limitations in reducing battery size could significantly increase the demand for these critical minerals. Conversely, scenarios like “Constrained nickel supply” and “Lower battery sizes” generally show lower demand, highlighting the importance of material availability and technological efficiency in shaping future mineral demand
# Load necessary librarieslibrary(ggplot2)library(dplyr)# Read the datasetdata <-read.csv("data/final_minerals_table_EV_modified.csv")key_years <-subset(data, year %in%c(2030, 2040))ggplot(key_years, aes(x=factor(year), y=value, fill=scenario)) +geom_bar(stat="identity", position=position_dodge()) +facet_wrap(~indicator, scales="free_y") +theme_minimal() +labs(title="Scenario Impact on Copper and Cobalt Demand",x="Year",y="Demand (kiloton)",fill="Scenario")
5 Copper and Cobalt Demand in the Net Zero Emissions by 2050 Scenario
These figures reveal a significant increase in demand for both Copper and Cobalt as we move towards achieving net zero emissions by 2050. The demand for Copper appears to peak around 2045 before slightly decreasing by 2050, while the demand for Cobalt shows a steady increase throughout the period, reflecting its critical role in the transition to cleaner energy and technologies. This scenario underscores the importance of these materials in supporting a sustainable future, highlighting the challenges and opportunities in securing supply chains and promoting recycling and sustainable mining practices.
library(ggplot2)library(dplyr)# Read the datasetdata <-read.csv("data/final_minerals_table_EV_modified.csv")# Filter and prepare datafiltered_data <- data %>%filter(scenario =="Net Zero Emissions by 2050 scenario", indicator %in%c("Copper", "Cobalt")) %>%group_by(year, indicator) %>%summarise(value =sum(value)) %>%# Replace sum with mean if average is more appropriateungroup() %>%mutate(year =as.factor(year))
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
# Calculate scale factor for Cobalt demand to align with Copper demand's scalemax_copper_value <-max(filtered_data$value[filtered_data$indicator =="Copper"])scale_factor <- max_copper_value /max(filtered_data$value[filtered_data$indicator =="Cobalt"])filtered_data <- filtered_data %>%mutate(adjusted_value =ifelse(indicator =="Cobalt", value * scale_factor, value))# Create the combined chartggplot(filtered_data) +geom_bar(data = filtered_data[filtered_data$indicator =="Copper",], aes(x = year, y = value, fill ="Copper"), stat ="identity") +geom_line(data = filtered_data[filtered_data$indicator =="Cobalt",], aes(x = year, y = adjusted_value, group =1, color ="Cobalt")) +geom_point(data = filtered_data[filtered_data$indicator =="Cobalt",], aes(x = year, y = adjusted_value, color ="Cobalt")) +scale_y_continuous(name ="Copper Demand (kiloton)", sec.axis =sec_axis(~./scale_factor, name ="Cobalt Demand (scaled)")) +labs(title ="Copper and Cobalt Demand in the Net Zero Emissions by 2050 Scenario", x ="Year") +scale_fill_manual(values =c("Copper"="orange")) +scale_color_manual(values =c("Cobalt"="blue")) +theme_minimal() +theme(legend.title =element_blank())
