As the world increasingly shifts towards renewable energy sources, my nation is taking decisive steps to transition from reliance on fossil fuels to establishing a more sustainable and resilient energy infrastructure. This pivot, with a pronounced emphasis on harnessing solar photovoltaic (PV) technology, is not only an integral component of our strategy to meet environmental sustainability objectives but also a pivotal move to counteract climate change and fortify our energy security. Nonetheless, this shift to green energy technologies brings to the fore significant concerns, particularly regarding the adequacy, reliability of supply chains, and the environmental ramifications associated with mining essential minerals such as copper and silicon, which are crucial for the production of solar PV systems.
This essay aims to delve into the intricate dynamics of mineral demand driven by the solar PV industry, with a special focus on understanding the critical roles and projected future demands of copper and silicon. This analysis is underpinned by insights and data from the International Energy Agency’s (IEA) 2023 Critical Minerals Report and the comprehensive IEA Database, providing a foundation for evaluating how these materials contribute to the evolving landscape of renewable energy and the challenges that lie ahead in ensuring their sustainable and ethical procurement.
# A tibble: 2 × 23
...1 ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9 ...10 ...11 ...12 ...13
<lgl> <dbl> <lgl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <chr> <dbl> <dbl>
1 NA NA NA State… NA NA NA NA NA NA Anno… NA NA
2 NA 2022 NA 2025 2030 2035 2040 2045 2050 NA 2025 2030 2035
# ℹ 10 more variables: ...14 <dbl>, ...15 <dbl>, ...16 <dbl>, ...17 <lgl>,
# ...18 <chr>, ...19 <dbl>, ...20 <dbl>, ...21 <dbl>, ...22 <dbl>,
# ...23 <dbl>
Tidying the headers
sheet_header_processed <- sheet_header |># transpose the datat() |># turn it back into a tibbleas_tibble() |># make them meaningfulrename(scenario = V1, year = V2) |># fill scenario downfill(scenario) |>#insert "Current" at topreplace_na(list(scenario ="Current Year"))
Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
`.name_repair` is omitted as of tibble 2.0.0.
ℹ Using compatibility `.name_repair`.
sheet_header_processed
# A tibble: 23 × 2
scenario year
<chr> <chr>
1 Current Year <NA>
2 Current Year 2022
3 Current Year <NA>
4 Stated policies scenario 2025
5 Stated policies scenario 2030
6 Stated policies scenario 2035
7 Stated policies scenario 2040
8 Stated policies scenario 2045
9 Stated policies scenario 2050
10 Stated policies scenario <NA>
# ℹ 13 more rows
# A tibble: 352 × 4
indicator cases_info_col_names value cases_name
<chr> <chr> <dbl> <chr>
1 Cadmium ...2 0.499 Comeback of high Cd-Te technology
2 Cadmium ...3 NA Comeback of high Cd-Te technology
3 Cadmium ...4 0.690 Comeback of high Cd-Te technology
4 Cadmium ...5 1.06 Comeback of high Cd-Te technology
5 Cadmium ...6 1.32 Comeback of high Cd-Te technology
6 Cadmium ...7 1.70 Comeback of high Cd-Te technology
7 Cadmium ...8 1.95 Comeback of high Cd-Te technology
8 Cadmium ...9 2.19 Comeback of high Cd-Te technology
9 Cadmium ...10 NA Comeback of high Cd-Te technology
10 Cadmium ...11 0.819 Comeback of high Cd-Te technology
# ℹ 342 more rows
combined_data <- cases_info_long |>left_join(sheet_headers_and_col_names, by =join_by(cases_info_col_names)) |># filter out what were empty columns (where years are NA) filter(!is.na(year)) |>mutate(# convert the year column from character to numericyear =as.integer(year) ) |>rename(mineral = indicator,tech_scenarios = cases_name,policy_scenarios = scenario) |>select(mineral, tech_scenarios, policy_scenarios, year, value)combined_data
# A tibble: 304 × 5
mineral tech_scenarios policy_scenarios year value
<chr> <chr> <chr> <int> <dbl>
1 Cadmium Comeback of high Cd-Te technology Current Year 2022 0.499
2 Cadmium Comeback of high Cd-Te technology Stated policies scenar… 2025 0.690
3 Cadmium Comeback of high Cd-Te technology Stated policies scenar… 2030 1.06
4 Cadmium Comeback of high Cd-Te technology Stated policies scenar… 2035 1.32
5 Cadmium Comeback of high Cd-Te technology Stated policies scenar… 2040 1.70
6 Cadmium Comeback of high Cd-Te technology Stated policies scenar… 2045 1.95
7 Cadmium Comeback of high Cd-Te technology Stated policies scenar… 2050 2.19
8 Cadmium Comeback of high Cd-Te technology Announced pledges scen… 2025 0.819
9 Cadmium Comeback of high Cd-Te technology Announced pledges scen… 2030 1.38
10 Cadmium Comeback of high Cd-Te technology Announced pledges scen… 2035 1.91
# ℹ 294 more rows
build the function
read_iea_solar_table <-function(cases_name_range, cases_info_range) { cases_name <-read_solar_sheet(range = cases_name_range) |>pull() cases_info <-read_solar_sheet(range = cases_info_range) cases_info_col_names <-names(cases_info) cases_info_long <- cases_info |>rename(indicator =`...1`) |>pivot_longer(cols =-indicator,names_to ="cases_info_col_names") |>add_column(cases_name) combined_data <- cases_info_long |>left_join(sheet_headers_and_col_names, by =join_by(cases_info_col_names)) |># filter out what were empty columns (where years are NA)filter(!is.na(year)) |># case_when is supercharged if elsemutate(# convert the year column from character to numericyear =as.integer(year) ) |>rename(mineral = indicator,tech_scenarios = cases_name,policy_scenarios = scenario) |>select(mineral, tech_scenarios, policy_scenarios, year, value) combined_data }
# A tibble: 1,216 × 5
mineral tech_scenarios policy_scenarios year value
<chr> <chr> <chr> <int> <dbl>
1 Cadmium Base case Current Year 2022 0.406
2 Cadmium Base case Stated policies scenario 2025 0.412
3 Cadmium Base case Stated policies scenario 2030 0.371
4 Cadmium Base case Stated policies scenario 2035 0.379
5 Cadmium Base case Stated policies scenario 2040 0.400
6 Cadmium Base case Stated policies scenario 2045 0.447
7 Cadmium Base case Stated policies scenario 2050 0.490
8 Cadmium Base case Announced pledges scenario 2025 0.489
9 Cadmium Base case Announced pledges scenario 2030 0.483
10 Cadmium Base case Announced pledges scenario 2035 0.548
# ℹ 1,206 more rows
Comparative Demand Trends
The projected future demand trends for copper and silicon are under three distinct policy scenarios: “Announced Pledges Scenario,” “Net Zero Emissions by 2050 Scenario,” and “Stated Policies Scenario.” These visualizations underscore the critical importance and escalating demand for these minerals in the context of global energy transitions.
In the “Announced Pledges Scenario,” both copper and silicon exhibit a significant upward trajectory in demand, highlighting the global commitment to renewable energy and the consequent increase in solar PV installations. Copper, starting from a demand value of approximately 1000 in 2025, experiences a steep rise, reaching nearly 1800 by 2050. Silicon shows a similar, though less pronounced, pattern, starting from around 1000 and approaching 1400 by 2050. This scenario reflects the impact of current governmental pledges on mineral demands, indicating a robust growth driven by the transition to renewable energy sources.
Under the “Net Zero Emissions by 2050 Scenario,” the demand curves for both minerals initially follow the upward trend seen in the first scenario, but with a notable peak and subsequent decline around 2040 for copper. This unusual pattern suggests a surge in copper demand for solar PV technology in the near to medium term, followed by a reduction, potentially due to advancements in recycling technologies or the advent of more efficient or alternative materials. Silicon, however, maintains a steadier demand increase, underscoring its consistent necessity in solar PV manufacturing without the same degree of peak and decline observed for copper.
The “Stated Policies Scenario” illustrates a more moderate but continuous upward trend for both minerals, reflecting the anticipated growth in demand based on currently implemented policies. Copper’s demand rises from around 1000 in 2025 to over 1100 by 2050, while silicon’s demand increases from below 1000 to just above 1000 in the same period. This trend underscores a steady but less aggressive growth compared to the other scenarios, suggesting that without stronger policy interventions, the shift towards renewable energy and the corresponding demand for critical minerals will be more gradual.
Therefore, policy decisions play a key role in shaping the future of energy infrastructure and mineral resource management.
install.packages("ggplot2")
The downloaded binary packages are in
/var/folders/cp/x4c86z354zq1gfn38__r36tc0000gn/T//RtmpSgb6XH/downloaded_packages
library(ggplot2)library(dplyr)# Filter out data for Copper and Silicon and exclude 'Current Year'filtered_data <- final_iea_solar_table %>%filter(mineral %in%c('Copper', 'Silicon')) %>%filter(policy_scenarios !='Current Year')# Plotting demand trends for Copper and Silicon under different policy scenariosggplot(filtered_data, aes(x = year, y = value, color = mineral)) +geom_smooth(method ='loess', se =FALSE) +# Apply LOESS smoothing, 'se' controls the confidence interval visibilityfacet_wrap(~policy_scenarios, scales ='free_y') +labs(title ="Future Demand Trends for Copper and Silicon under Different Policy Scenarios",x ="Year",y ="Demand (Value)",color ="Mineral") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
`geom_smooth()` using formula = 'y ~ x'
Growth Rate Comparison within the “Comeback of high Cd-Te technology” tech scenario, and then create the smoothed Year-on-Year Growth Rates graph for these specific conditions, you ca
`summarise()` has grouped output by 'policy_scenarios'. You can override using
the `.groups` argument.
# Plotting impact of policy scenarios on total demandggplot(scenario_impact_data, aes(x = policy_scenarios, y = total_demand, fill = mineral)) +geom_bar(stat ="identity", position ="dodge") +labs(title ="Impact of Policy Scenarios on Total Demand for Copper and Silicon",x ="Policy Scenario",y ="Total Demand (Sum of Values)",fill ="Mineral") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
Future Demand Trends for Copper and Silicon under Different Policy Scenarios
The intensity and ambition of energy and environmental policies significantly influence the total demand for critical minerals like copper and silicon, essential for the transition to renewable energy.
The aggregate demand for these essential minerals under three different policy frameworks are: the “Announced Pledges Scenario,” “Net Zero Emissions by 2050 Scenario,” and “Stated Policies Scenario.” This bar chart distinctly demonstrates that the total demand for copper and silicon is substantially influenced by the stringency and ambition of environmental and energy policies.
In the “Announced Pledges Scenario,” we observe substantial demand for both minerals, with copper showing slightly higher total demand compared to silicon. This reflects the anticipated increase in the utilization of solar PV technologies and other renewable resources, as countries begin to act on their climate pledges. However, the scenario also implies that while significant, the commitments are not pushing the demand for these minerals to their maximum potential.
The “Net Zero Emissions by 2050 Scenario” presents the most striking figures, with the highest total demand for both minerals, illustrating the aggressive shift towards renewable energy technologies, notably solar PV, required to achieve net-zero targets. The notably higher demand under this scenario underscores the intensive mineral requirements associated with rapid and comprehensive energy transition efforts. Copper, essential for electrical conductivity in renewable systems, and silicon, crucial for solar cells, are both in significantly higher demand, highlighting the critical role of robust policy frameworks in driving the transition towards sustainable energy.
In contrast, the “Stated Policies Scenario” demonstrates a more moderate increase in mineral demand, reflecting a continuation of current trends and policies without additional ambitious commitments. This suggests that while there is growth, it may not be sufficient to meet the global targets for reducing greenhouse gas emissions and combating climate change.
Hence, policy plays a crucial role in shaping the future demand for critical minerals like copper and silicon. Furthermore, we need more ambitious policies to accelerate the transition to renewable energy and sustainably manage the mineral resources required for this transformation.
library(ggplot2)library(dplyr)# Assuming your data is stored in 'final_iea_solar_table' and you've already filtered out 'Current Year'filtered_data <- final_iea_solar_table %>%filter(mineral %in%c('Copper', 'Silicon')) %>%filter(policy_scenarios !='Current Year') %>%filter(tech_scenarios =="Base case") # Adding filter for 'Base case' in tech scenarios# Plotting demand trends with smoothed lines for Copper and Silicon under different policy scenariosggplot(filtered_data, aes(x = year, y = value, color = mineral)) +geom_smooth(method ='loess', se =FALSE) +# Apply LOESS smoothing, 'se' controls the confidence interval visibilityfacet_wrap(~policy_scenarios, scales ='free_y') +labs(title ="Smoothed Future Demand Trends for Copper and Silicon under Different Policy Scenarios (Base Case)",x ="Year",y ="Demand (Value)",color ="Mineral") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
`geom_smooth()` using formula = 'y ~ x'
the impact of policy scenarios on the total demand for Copper and Silicon for each tech scenario separately
`summarise()` has grouped output by 'policy_scenarios'. You can override using
the `.groups` argument.
ggplot(base_case_data, aes(x = policy_scenarios, y = total_demand, fill = mineral)) +geom_bar(stat ="identity", position ="dodge") +labs(title ="Impact of Policy Scenarios on Copper and Silicon Demand (Base Case)",x ="Policy Scenario",y ="Total Demand (Sum of Values)",fill ="Mineral") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
# Comeback of high Cd-Te technology excluding 'Current Year'cd_te_data <- final_iea_solar_table %>%filter(mineral %in%c('Copper', 'Silicon')) %>%filter(tech_scenarios =="Comeback of high Cd-Te technology") %>%filter(policy_scenarios !="Current Year") %>%# Exclude 'Current Year' datagroup_by(policy_scenarios, mineral) %>%summarize(total_demand =sum(value, na.rm =TRUE))
`summarise()` has grouped output by 'policy_scenarios'. You can override using
the `.groups` argument.
# Plot for Comeback of high Cd-Te technology without 'Current Year'ggplot(cd_te_data, aes(x = policy_scenarios, y = total_demand, fill = mineral)) +geom_bar(stat ="identity", position ="dodge") +labs(title ="Impact of Policy Scenarios on Copper and Silicon Demand (Comeback of high Cd-Te)",x ="Policy Scenario",y ="Total Demand (Sum of Values)",fill ="Mineral") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
# Wider adoption of perovskite solar cells excluding 'Current Year'perovskite_data <- final_iea_solar_table %>%filter(mineral %in%c('Copper', 'Silicon')) %>%filter(tech_scenarios =="Wider adoption of perovskite solar cells") %>%filter(policy_scenarios !="Current Year") %>%# Exclude 'Current Year' datagroup_by(policy_scenarios, mineral) %>%summarize(total_demand =sum(value, na.rm =TRUE), .groups ='drop') # Including .groups = 'drop' to prevent grouping message# Plot for Wider adoption of perovskite solar cells without 'Current Year'ggplot(perovskite_data, aes(x = policy_scenarios, y = total_demand, fill = mineral)) +geom_bar(stat ="identity", position ="dodge") +labs(title ="Impact of Policy Scenarios on Copper and Silicon Demand (Wider adoption of perovskite solar cells)",x ="Policy Scenario",y ="Total Demand (Sum of Values)",fill ="Mineral") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
The total demand for copper and silicon varies significantly under different energy policy scenarios and technological developments, affecting the future landscape of renewable energy infrastructure.
The provided R code and corresponding graphs offer an insightful comparative analysis of how different policy scenarios and technological advancements impact the total demand for copper and silicon. The “Base Case” scenario depicts how initial policy commitments, labeled as “Announced Pledges Scenario,” trigger a noticeable increase in demand for both minerals, essential for solar PV systems. This scenario suggests a proactive yet measured approach towards renewable energy adoption.
The second set of data visualizations reflects a “Comeback of high Cd-Te Technology,” which represents a technological shift possibly decreasing the demand for silicon due to the adoption of alternative photovoltaic materials. Despite this, copper demand remains relatively stable across scenarios, highlighting its indispensable role in the renewable sector.
In the final set, “Wider adoption of perovskite solar cells,” demonstrates an alternative technological path that could lead to different demand dynamics for copper and silicon. Here, the demand for silicon might decrease if perovskite solar cells, which do not require silicon, become more prevalent.
These graphical analyses underscore the dynamic interplay between policy decisions, technological advancements, and mineral demand. They reveal that while policy initiatives significantly drive demand for these critical minerals, technological innovations can dramatically alter these trajectories. This nuanced understanding is crucial for developing strategies that ensure sustainable and secure supply chains for the renewable energy sector.
Conclusion
In conclusion, the demand for critical minerals like copper and silicon is heavily influenced by policy frameworks and technological advancements. The visualizations and analyses demonstrate that aggressive and ambitious policy scenarios lead to higher demand, underscoring the need for comprehensive strategies that support renewable energy transitions while considering the impacts of new technologies. As we move forward, it is crucial to balance policy ambitions with technological innovation to ensure sustainable and secure supply chains for the renewable energy sector, ultimately contributing to the global effort against climate change.
