Significant implementation gap exists between current policies and rapid CO2 reduction necessary to meet the Paris Agreement goal — achieving net zero emissions by 2050.
Electricity generation accounts for about three-quarters of global CO2 emissions and thus is the biggest target for carbon reduction efforts.
Solar PVs are an important renewable energy source to achieve the rapid carbon reduction.
A quadratic increase in solar PV production will help us reach the NZE and STEPS levels of Solar PV demand and a cubic increase is enough to set us on track for a 100% solar scenario.
1. Introduction
According to the Paris Agreement, the goal of net zero by 2050 is critical for countries to achieve a limit to the global temperature rise to 1.5 degrees Celsius. Although 195 countries have already ratified the Paris Agreement and its ambitious goals, 140 countries have even pledged to achieve Net Zero emissions by around 2050 (NZE) (UN, 2024). According to the U.S. Energy Information Administration, energy generation accounts for about three-quarters of global CO2 emissions. However, our current efforts still fall short of what is necessary to meet those goals, indicating that our transition to a fully renewable energy system needs to accelerate. As such, a significant reduction in energy sector-related emissions—which primarily come from fossil-fuel-based electricity generation—is crucial to substantially reducing global CO2 emissions. The recent solar PV oversupply presents (Business Insider, 2024) a significant opportunity as electricity demand increases with the global shift toward a green economy. As such, this paper explores the supply and demand dynamics of Solar PVs up to 2050.
More specifically, we conduct a thought experiment to examine whether the solar PV industry can match the solar PV count necessary to reach the net zero goal by 2050. We structure our analysis as follows:
Highlighting actions for rapid CO2 reductions to meet the 2050 Net Zero goals.
Projecting the demand for Solar PVs under three scenarios: the Stated Policy scenario, the Net Zero Emissions by 2050 scenario, and a 100% Solar PV scenario.
Analyzing the CO2 reduction in each solar PV scenario.
Assessing the capacity of the solar PV industry to meet the required Solar PV count for achieving the net zero target by 2050.
2. Actions to achieve NZE by 2050
Given current emissions of 37,000 tonnes a year, achieving net zero emissions by 2050 is challenging in the prevailing policy landscape. Rapid CO2 reduction efforts are necessary to reach the Paris Agreement 1.5C goal. However, the current policy landscape, reflected by the IEA’s Stated Policies Scenario, shows that the world is substantially behind on CO2 reduction, projected to reach 30,000 tonnes of annual emissions by 2050 instead of the net zero target, creating a massive implementation gap, as illustrated in Figure 1.
Figure 1. Global CO2 Emissions from 1900 to 2050
To successfully meet the NZE by 2050 goals, we must achieve two separate goals:
Boost energy supply in order to meet the growing electricity demand due to increasing development and electrification of the economy.
Reduce carbon emissions to net zero through transitioning to green energy.
2.1 Reducing Carbon Emissions
We use the Stated Policies Scenario (STEPS), Net Zero Emissions by 2050 Scenario (NZE), and the 100% Solar Scenario to evaluate the energy demand and solar capacity demand needed to reach energy demand goals.
2.1.1 Stated Policies Scenario
The STEPS provides the prevailing direction of energy system progression based on a detailed review of the current policy landscape. It provides a sector-by-sector evaluation of the policies put in place to reach stated goals and other energy-related objectives, with both existing policies and measures and those under development. The STEPS provides a more conservative benchmark for the future. While solar PV is projected to be one of the main electricity sources by 2050 under the STEPS, it is only one among many other sources and thus has lower
2.1.2 Net Zero Emissions by 2050 Scenario
The NZE Scenario is a normative scenario that shows a pathway for the global energy sector to achieve net zero CO2 emissions by 2050. This scenario also meets key energy-related Sustainable Development Goals (SDGs), particularly universal energy access by 2030 and major improvements in air quality. It is consistent with limiting the global temperature rise to 1.5 °C. While Solar plays a major role in achieving the electricity capacity and demand under this scenario, it is not the sole source of electricity, and is complemented by wind, hydro, and other forms of energy sources.
2.1.3 Solar 100% Solar PVs Scenario
Our analysis also includes an extreme scenario in which all electricity generation is entirely powered by solar PVs, representing a case where all sectors utilize green energy. This scenario primarily serves to establish an upper bound on the potential future demand for solar PVs. By doing so, it allows us to examine and compare the maximal PV production requirements against our projections. This exploration is crucial for understanding the upper limits of solar PV adoption and ensuring that production capabilities can meet or exceed future demands in a fully renewable energy landscape. Under this scenario, we use the total electricity demand given in the NZE scenario as that is the highest projected electricity generation capacity which needs to be achieved by 2050.
2.2 Meeting Electricity Demand
Figure 2 presents a comparative analysis of projected electrical capacities under the STEPS and the NZE. As stated above, the 100% Solar Scenario follows the electricity demand of the NZE. The NZE scenario indicates a substantial intensification of efforts to expand electrical capacity, likely reflecting a significant increase in renewable energy investments and a concerted push towards the decarbonization of the power sector. This visual comparison highlights the pivotal role of policy and innovation in molding the future energy landscape, ensuring alignment with global climate targets, and emphasizing the necessity of transitioning to sustainable energy sources.
Figure 2. Comparing total electricity capacity in different scenarios until 2050
3. Role of Solar PVs in Energy sector
Countries are at various stages in developing their solar capacity, with Australia, Brazil, China, India, and the US being the frontrunners in terms of installed capacity. Map 1 provides a visual analysis of the solar power landscape in 2022 by presenting the percentile rankings of countries in terms of their installed solar photovoltaic (PV) capacity. This depiction is instrumental in assessing each nation’s ability to meet the Net Zero emissions target by 2050. The data, sourced from the World Population Review, indicates the solar electricity capacity of each country in megawatts and reveals a marked contrast in solar energy production capabilities around the globe. Notably, nations such as Australia, Brazil, China, India, the United States, and several European countries have emerged as frontrunners in harnessing solar energy due to their equatorial proximity, substantial energy needs, and advanced solar PV technology access. The map points to a divide where, despite certain countries making significant strides in exploiting their solar potential, others need to undertake considerable policy changes and investments to enhance their solar PV generation infrastructure. One caveat is that we are missing data for a substantial portion of the developing world.
Map 1. Percentile Ranking of Major Solar Power Producing Countries
(Click on the map to interact)
3.1 Solar Capacity under Three Scenarios Projection
We compare the trajectory of electrical capacity development compared under three scenarios in Figure 3. The STEPS scenario illustrates a more conservative growth, reflective of current and announced policy frameworks, projected to reach only 12639 GW by 2050. The NZE scenario shows robust growth in solar capacity, aligning with vigorous policy measures aiming for net zero emissions by 2050, reaching 18753 GW. In contrast, the Full Solar scenario projects a steep increase, demonstrating an aggressive expansion and potential full reliance on solar power for electricity generation, and the goal is to reach 36956 GW by 2050, which is the amount of total electricity capacity projected under NZE by 2050.
Figure 3. Solar Capacity by Scenario 2010-2050
To compare with the projected supply, which is represented by Solar PV production in our analysis, an annual increase in electrical capacity has been modeled to show quadratic growth, as illustrated in Figure 4. The Full Solar scenario shows a remarkable quadratic increase where the annual increase will reach approximately 4700 GW by 2050, indicating a theoretical situation where solar power becomes the sole source of electricity, representing the upper limits of solar technology adoption. The NZE scenario also demonstrates significant growth, but less steep compared to the Full Solar scenario, which may correspond with realistic policy goals aimed at a sustainable mix of energy sources, which expects the annual increase in 2050 will reach around 1800 GW. Meanwhile, the STEPS scenario displays a more modest increase, reflective of current policies and their anticipated gradual impact on capacity enhancement, which projects the annual increase of electricity capacity in 2050 to be only 1000 GW.
Figure 4. Average Yearly Increase in Capacity with Exponential Growth by Scenario
3.2 Carbon Emissions under three scenarios
Under all three scenarios, we can avoid between 16,000 and 60,000 tonnes of CO2 emissions by 2050. To evaluate the impact of solar photovoltaics (PV) on achieving the goal of net-zero emissions by 2050, we projected the carbon emissions avoided by transitioning to solar power, showcased in Figure 5. We adopted the calculation that every 1 GW of electricity produced accounts for 0.7716 Mt of carbon, as provided by the IEA (2020).
The 100% Solar Power scenario, depicted by the orange line, shows the most significant reduction in emissions, suggesting a progressive and substantial impact on CO2 emissions if solar power were to be fully adopted. The NZE 2050 scenario, illustrated by the green line, predicts a moderate reduction in emissions, while the STEPS scenario, indicated by the red line, shows the least amount of CO2 emissions avoidance. It presents the potential of solar power to significantly mitigate CO2 emissions and highlights the varying degrees of impact each energy policy scenario could have on the environment. Among all renewable energies, solar PV does not contribute the most to avoiding carbon emissions; nuclear power, wind power, and hydro power avoid even more carbon emissions.
Figure 5. Avoided CO2 emission with Electricity generated by Solar PV projection
4. Solar PV Supply versus Demand projection
Global solar PV production will reach 2300 gigawatts (by current trend estimate). The range is between 500 gigawatts (conservative estimate) to 8100 gigawawtts (extreme estimate) by 2050. Using historical data from Statista, we estimate future global PV production trends using an quadratic projection (Figure 6), which is a rational estimate with the current trend. A range is also estimated using a linear model and a cubed model (Figure 7), providing the lower bound and the upper bound of the projected global PV production. Here, the Quadratic model (Figure 6) projects that by 2050, solar PV production will reach 2300 gigawatts annually, which fits well. Figure 7 shows a linear and a cubed projections until 2050, which is simply to provide a range since it is unrealistic to reach both ends. Under this scenario, annual production of Solar PVs will reach about 2300 GW by 2050.
Both Figure 6 and Figure 7 show a clear outlier for 2022, visible in the linear and quadratic trends pictured here. This substantial hike in Solar PV supply is likely due to China’s overproduction of solar PVs in recent years and signals a This chart confirms that China’s production has far surpassed the global demand for solar PVs.
Figure 6. Global Solar PV Production from 2000-2050 with Quadratic Projection
Figure 7. Global Solar PV Production from 2000-2050 with Linear Projection vs Cubed Projection
When comparing supply versus demand (Figure 8.), the graph on the left, titled “Global PV Production Projected to 2050,” represents the supply side, showing the annual increase in electricity generated by solar PVs. The graph on the right, titled “Global Annual Increase in Capacity,” represents the demand side, illustrating the projected increase in global electricity capacity within the solar PV sector.
Based on our findings from the previous section, if the production of solar PVs increases quadratically, the supply side will be able to meet the maximum scenario demand under the NZE. This is because the annual increase in electricity capacity demanded by 2050 is around 1800 GW, which is well within the reach of the 2300 GW projected for annual solar PV production increases. In the extreme scenario where 100% of energy comes from solar PVs, the annual increase in electricity capacity demand is expected to reach around 4400 GW. This demand could be met if solar PV production were to follow a cubic growth trend. However, since the 100% solar PV scenario is improbable, solar PV production merely needs to grow quadratically to satisfy future demand.
Figure 8. Projected Yearly Increases in Electrical Capacity vs Global PV Production Quadratic Projection
Conclusion
In conclusion, current policies are not on track to meet the NZE scenario and greening the energy sector is the key to closing this implementation gap. Solar PVs are an important resource to achieve the rapid carbon reduction. We found that solely by reaching 100% Solar power by 2050 or the NZE scenario solar capacity, we can reduce enough emissions to get very close to or beyond net zero. Furthermore, we found a quadratic increase in solar PV production will help us reach the NZE and STEPS levels of Solar PV demand and a cubic increase is enough to set us on track for even the full solar scenario.
---title: "Ding & Ticha Sustainable Finance Final Project"format: htmleditor: visualcode-fold: falsecode-tools: true---# Solar PVs projected Supply vs Demand### Executive Summary- Significant **implementation gap** exists between current policies and rapid CO2 reduction necessary to meet the Paris Agreement goal — achieving net zero emissions by 2050.- Electricity generation accounts for about **three-quarters of global CO2 emissions** and thus is the biggest target for carbon reduction efforts.- Solar PVs are an important renewable energy source to achieve the rapid carbon reduction.- A **quadratic increase in solar PV production** will help us reach the NZE and STEPS levels of Solar PV demand and **a cubic increase** is enough to set us on track for a 100% solar scenario.### 1. IntroductionAccording to the Paris Agreement, the goal of net zero by 2050 is critical for countries to achieve a limit to the global temperature rise to 1.5 degrees Celsius. Although 195 countries have already ratified the Paris Agreement and its ambitious goals, 140 countries have even pledged to achieve Net Zero emissions by around 2050 (NZE) (UN, 2024). According to the U.S. Energy Information Administration, energy generation accounts for about three-quarters of global CO2 emissions. However, our current efforts still fall short of what is necessary to meet those goals, indicating that our transition to a fully renewable energy system needs to accelerate. As such, a significant reduction in energy sector-related emissions—which primarily come from fossil-fuel-based electricity generation—is crucial to substantially reducing global CO2 emissions. The recent solar PV oversupply presents (Business Insider, 2024) a significant opportunity as electricity demand increases with the global shift toward a green economy. As such, **this paper explores the supply and demand dynamics of Solar PVs up to 2050.**More specifically, we conduct a thought experiment to examine whether the solar PV industry can match the solar PV count necessary to reach the net zero goal by 2050. We structure our analysis as follows: - Highlighting actions for rapid CO2 reductions to meet the 2050 Net Zero goals.- Projecting the demand for Solar PVs under three scenarios: the Stated Policy scenario, the Net Zero Emissions by 2050 scenario, and a 100% Solar PV scenario. - Analyzing the CO2 reduction in each solar PV scenario.- Assessing the capacity of the solar PV industry to meet the required Solar PV count for achieving the net zero target by 2050.### 2. Actions to achieve NZE by 2050**Given current emissions of 37,000 tonnes a year, achieving net zero emissions by 2050 is challenging in the prevailing policy landscape**. Rapid CO2 reduction efforts are necessary to reach the Paris Agreement 1.5C goal. However, the current policy landscape, reflected by the IEA's Stated Policies Scenario, shows that the world is substantially behind on CO2 reduction, projected to reach 30,000 tonnes of annual emissions by 2050 instead of the net zero target, creating a massive implementation gap, as illustrated in Figure 1.##### *Figure 1. Global CO2 Emissions from 1900 to 2050*```{r}#| echo: false#| message: false#| warning: false# options(repos = c(CRAN = "https://cran.rstudio.com/"))library(tidyverse)library(readxl)library(here)library(ggplot2)library(patchwork)library(dplyr)library(plotly)library(rnaturalearth)library(rnaturalearthdata)library(plotly)library(scales)library(leaflet)library(broom)library(patchwork)``````{r}#| echo: falsepath_to_sheet <-here("data_raw", "CO2 data.xlsx")CO2_data <-partial(.f = read_excel,path = path_to_sheet,sheet ="CO2",col_names =FALSE)CO2_data <-suppressMessages(CO2_data(range ="B2:C274"))CO2_data <- CO2_data |>rename(year =`...1`, CO2_emissions =`...2`)CO2_data$scenario <-"Historical"path_to_sheet <-here("data_raw", "WEO2023_Free_Dataset.xlsx")CO2proj_data <-partial(.f = read_excel,path = path_to_sheet,sheet ="World CO2 Emissions",col_names =FALSE)CO2_STEPS_data <-suppressMessages(CO2proj_data(range ="N7:Q8"))CO2_NZE_data <-suppressMessages(CO2proj_data(range ="AF7:AI8"))CO2_NZE_data <-t(CO2_NZE_data)|>as.data.frame()CO2_STEPS_data <-t(CO2_STEPS_data)|>as.data.frame()colnames(CO2_STEPS_data) <-c('year', 'CO2_emissions')colnames(CO2_NZE_data) <-c('year', 'CO2_emissions')combined_CO2_STEPS<-bind_rows(CO2_data, CO2_STEPS_data)|>filter(year >1900)combined_CO2_STEPS$scenario <-"STEPS"combined_CO2_NZE<-bind_rows(CO2_data, CO2_NZE_data)|>filter(year >1900)combined_CO2_NZE$scenario <-"NZE 2050"combined_CO2<-bind_rows(combined_CO2_NZE, combined_CO2_STEPS)p1 <-ggplot(combined_CO2, aes(x = year, y = CO2_emissions, color = scenario)) +geom_line()+scale_color_manual(values =c("NZE 2050"="#008000", "STEPS"="red")) +labs(title ="Global CO2 Emissions from 1900 to 2050",subtitle ="An assessment on future projections under STEPS vs NZE 2050 scenario",x ="Year",y ="CO2 Emissions (in tonnes)",color ="Scenario") +theme_minimal(base_size =14) +theme(plot.title =element_text(face ="bold", size =18), plot.subtitle =element_text(face ="italic", size =14), plot.caption =element_text(size =8), axis.title =element_text(size =14),axis.text =element_text(size =12, angle =45, hjust=1), legend.position ="bottom") +scale_x_continuous(breaks =seq(1900, 2050, by =10)) +scale_y_continuous(labels = scales::comma) interactive_plot <-ggplotly(p1)|>layout(title =list(text ='Global CO2 Emissions from 1900 to 2050<br><sub>An assessment of historical data and NZE by 2050 scenario projections</sub>',x =0.5,xanchor ='center',yanchor ='top' ) )interactive_plot```**To successfully meet the NZE by 2050 goals, we must achieve two separate goals:**1. **Boost energy supply** in order to meet the growing electricity demand due to increasing development and electrification of the economy.2. **Reduce carbon emissions** to net zero through transitioning to green energy.#### 2.1 Reducing Carbon EmissionsWe use the Stated Policies Scenario (STEPS), Net Zero Emissions by 2050 Scenario (NZE), and the 100% Solar Scenario to evaluate the energy demand and solar capacity demand needed to reach energy demand goals.##### 2.1.1 Stated Policies Scenario**The STEPS provides the prevailing direction of energy system progression based on a detailed review of the current policy landscape.** It provides a sector-by-sector evaluation of the policies put in place to reach stated goals and other energy-related objectives, with both existing policies and measures and those under development. The STEPS provides a more conservative benchmark for the future. While solar PV is projected to be one of the main electricity sources by 2050 under the STEPS, it is only one among many other sources and thus has lower##### 2.1.2 Net Zero Emissions by 2050 Scenario**The NZE Scenario is a normative scenario that shows a pathway for the global energy sector to achieve net zero CO2 emissions by 2050.** This scenario also meets key energy-related Sustainable Development Goals (SDGs), particularly universal energy access by 2030 and major improvements in air quality. It is consistent with limiting the global temperature rise to 1.5 °C. While Solar plays a major role in achieving the electricity capacity and demand under this scenario, it is not the sole source of electricity, and is complemented by wind, hydro, and other forms of energy sources.##### 2.1.3 Solar 100% Solar PVs Scenario**Our analysis also includes an extreme scenario in which all electricity generation is entirely powered by solar PVs, representing a case where all sectors utilize green energy.** This scenario primarily serves to establish an upper bound on the potential future demand for solar PVs. By doing so, it allows us to examine and compare the maximal PV production requirements against our projections. This exploration is crucial for understanding the upper limits of solar PV adoption and ensuring that production capabilities can meet or exceed future demands in a fully renewable energy landscape. Under this scenario, we use the total electricity demand given in the NZE scenario as that is the highest projected electricity generation capacity which needs to be achieved by 2050.#### 2.2 Meeting Electricity DemandFigure 2 presents a comparative analysis of projected electrical capacities under the STEPS and the NZE. As stated above, the 100% Solar Scenario follows the electricity demand of the NZE. The NZE scenario indicates a substantial intensification of efforts to expand electrical capacity, likely reflecting a significant increase in renewable energy investments and a concerted push towards the decarbonization of the power sector. This visual comparison highlights the pivotal role of policy and innovation in molding the future energy landscape, ensuring alignment with global climate targets, and emphasizing the necessity of transitioning to sustainable energy sources.##### *Figure 2. Comparing total electricity capacity in different scenarios until 2050*```{r}#| echo: falseWEO_Values_long <-read_excel("data_raw/WEO_Values_long")total_capacity <- WEO_Values_long %>%filter(Type =="Total", Description =="Electrical capacity", Scenario !="APS") %>%mutate(Values =as.numeric(as.character(Values)),Year =as.numeric(Year))g3<-ggplot(total_capacity, aes(x = Year, y = Values, colour = Scenario)) +geom_line() +scale_color_manual(values =c("NZE"="darkgreen", "STEPS"="red"),name ="Scenario" ) +labs(title ="Total Electrical Capacity 2000-2050",subtitle ="Comparing different scenarios until 2050",x ="Year",y ="Electrical Capacity (GW)" ) +theme_minimal() +theme(plot.title =element_text(hjust =0.5, face ="bold"),plot.subtitle =element_text(hjust =0.5),legend.title =element_text(face ="bold"),legend.position ="bottom",axis.text.x =element_text(angle =45, hjust =1),axis.title.x =element_text(vjust =-0.2),axis.title.y =element_text(vjust =2),legend.text =element_text(size =8) # Set size of legend text if necessary ) +scale_y_continuous(labels = comma) +# Format y-axis labels with commas for thousandsscale_x_continuous(breaks =seq(2000, 2050, 5)) # Ensure all years up to 2050 are coveredggplotly(g3)```### 3. Role of Solar PVs in Energy sector**Countries are at various stages in developing their solar capacity, with Australia, Brazil, China, India, and the US being the frontrunners in terms of installed capacity.** Map 1 provides a visual analysis of the solar power landscape in 2022 by presenting the percentile rankings of countries in terms of their installed solar photovoltaic (PV) capacity. This depiction is instrumental in assessing each nation's ability to meet the Net Zero emissions target by 2050. The data, sourced from the World Population Review, indicates the solar electricity capacity of each country in megawatts and reveals a marked contrast in solar energy production capabilities around the globe. Notably, nations such as Australia, Brazil, China, India, the United States, and several European countries have emerged as frontrunners in harnessing solar energy due to their equatorial proximity, substantial energy needs, and advanced solar PV technology access. The map points to a divide where, despite certain countries making significant strides in exploiting their solar potential, others need to undertake considerable policy changes and investments to enhance their solar PV generation infrastructure. One caveat is that we are missing data for a substantial portion of the developing world.##### *Map 1. Percentile Ranking of Major Solar Power Producing Countries*##### *(Click on the map to interact)*[](https://rpubs.com/JD98/1177709)#### 3.1 Solar Capacity under Three Scenarios ProjectionWe compare the trajectory of electrical capacity development compared under three scenarios in Figure 3. The STEPS scenario illustrates a more conservative growth, reflective of current and announced policy frameworks, projected to reach only 12639 GW by 2050. The NZE scenario shows robust growth in solar capacity, aligning with vigorous policy measures aiming for net zero emissions by 2050, reaching 18753 GW. In contrast, the Full Solar scenario projects a steep increase, demonstrating an aggressive expansion and potential full reliance on solar power for electricity generation, and the goal is to reach 36956 GW by 2050, which is the amount of total electricity capacity projected under NZE by 2050.##### *Figure 3. Solar Capacity by Scenario 2010-2050*```{r}#| echo: falseWEO_Values_long <- WEO_Values_long %>%mutate(Year =as.numeric(Year),Values =as.numeric(Values))NZE_PV_scenario <- WEO_Values_long %>%filter(Description =="Electrical capacity") %>%filter(Scenario =="NZE") %>%filter(Type =="Solar PV")pv_values_before_2023 <- WEO_Values_long %>%filter(Year <2023& Scenario =="NZE"& Type =="Solar PV"& Description =="Electrical capacity")# Filter for year 2050 with "Total" Typetotal_value_2050 <- WEO_Values_long %>%filter(Year ==2050& Scenario =="NZE"& Type =="Total"& Description =="Electrical capacity")# Combine the two dataframesfull_PV_scenario <-bind_rows(pv_values_before_2023, total_value_2050) %>%mutate(Scenario ="Full Solar")STEPS_PV_scenario <- WEO_Values_long %>%filter(Description =="Electrical capacity") %>%filter(Scenario =="STEPS") %>%filter(Type =="Solar PV")all_solar_scenarios <-bind_rows( full_PV_scenario, NZE_PV_scenario, STEPS_PV_scenario)space_format <-function() {function(x) {format(x, big.mark =" ", scientific =FALSE, trim =TRUE) }}solar_scenarios_plot <-ggplot(all_solar_scenarios, aes(x = Year, y = Values, color = Scenario)) +geom_line() +scale_color_manual(values =c("Full Solar"="orange", "NZE"="darkgreen", "STEPS"="red")) +labs(title ="Solar Capacity by Scenario 2010-2050",x ="Year",y ="Electrical Capacity in GW" ) +theme_minimal() +scale_y_continuous(labels =space_format())ggplotly(solar_scenarios_plot)```To compare with the projected supply, which is represented by Solar PV production in our analysis, an annual increase in electrical capacity has been modeled to show quadratic growth, as illustrated in Figure 4. The Full Solar scenario shows a remarkable quadratic increase where the annual increase will reach approximately 4700 GW by 2050, indicating a theoretical situation where solar power becomes the sole source of electricity, representing the upper limits of solar technology adoption. The NZE scenario also demonstrates significant growth, but less steep compared to the Full Solar scenario, which may correspond with realistic policy goals aimed at a sustainable mix of energy sources, which expects the annual increase in 2050 will reach around 1800 GW. Meanwhile, the STEPS scenario displays a more modest increase, reflective of current policies and their anticipated gradual impact on capacity enhancement, which projects the annual increase of electricity capacity in 2050 to be only 1000 GW.##### *Figure 4. Average Yearly Increase in Capacity with Exponential Growth by Scenario*```{r}#| echo: falselibrary(ggplot2)library(dplyr)library(tidyr)scenario_summary <-data.frame(Scenario =c("Full Solar", "NZE", "STEPS"),Value_2022 =c(1144.88, 1144.88, 1144.88),Value_2050 =c(36956.46, 18753.48, 12639.19))# Calculate the Quadratic annual growth ratescenario_summary <- scenario_summary %>%mutate(GrowthRate = (Value_2050 / Value_2022)^(1/ (2050-2022)) -1 )# Now create a new dataframe to hold the yearly values for each scenarioyears <-2022:2050yearly_values <-expand.grid(Year = years, Scenario = scenario_summary$Scenario)# Join the data with the growth rates and base valueyearly_values <-left_join(yearly_values, scenario_summary, by ="Scenario")# Calculate the yearly values with Quadratic growthyearly_values <- yearly_values %>%mutate(TotalCapacity = Value_2022 * ((1+ GrowthRate)^(Year -2022)),PreviousYearCapacity =if_else(Year ==min(years), Value_2022, Value_2022 * ((1+ GrowthRate)^((Year -1) -2022))) ) %>%mutate(YearlyIncrease = TotalCapacity - PreviousYearCapacity )# Select only the columns you needyearly_values <-select(yearly_values, Scenario, Year, YearlyIncrease)# Plot the yearly increasesp4 <-ggplot(yearly_values, aes(x = Year, y = YearlyIncrease, color = Scenario)) +geom_line() +scale_color_manual(values =c("Full Solar"="orange", "NZE"="darkgreen", "STEPS"="red")) +geom_text(data =subset(yearly_values, Year ==2050), aes(label = Scenario), hjust =1.5, vjust =0) +labs(title ="Projected Increases in Electrical Capacity",x ="Year", y ="Growth in Electrical Capacity (GW)") +theme_minimal(base_size =14) +theme(plot.title =element_text(face ="bold", hjust =0.5, size =11),plot.subtitle =element_text(hjust =0.5),axis.title =element_text(face ="bold"),axis.text =element_text(color ="#112446"),axis.text.x =element_text(angle =45, hjust =1),axis.title.x =element_text(vjust =-0.2),axis.title.y =element_text(vjust =2),panel.grid.major.x =element_blank(),panel.grid.minor.x =element_blank(),legend.position ="none"# This will remove the legend )print(p4)```#### 3.2 Carbon Emissions under three scenarios**Under all three scenarios, we can avoid between 16,000 and 60,000 tonnes of CO2 emissions by 2050.** To evaluate the impact of solar photovoltaics (PV) on achieving the goal of net-zero emissions by 2050, we projected the carbon emissions avoided by transitioning to solar power, showcased in Figure 5. We adopted the calculation that every 1 GW of electricity produced accounts for 0.7716 Mt of carbon, as provided by the IEA (2020).The 100% Solar Power scenario, depicted by the orange line, shows the most significant reduction in emissions, suggesting a progressive and substantial impact on CO2 emissions if solar power were to be fully adopted. The NZE 2050 scenario, illustrated by the green line, predicts a moderate reduction in emissions, while the STEPS scenario, indicated by the red line, shows the least amount of CO2 emissions avoidance. It presents the potential of solar power to significantly mitigate CO2 emissions and highlights the varying degrees of impact each energy policy scenario could have on the environment. Among all renewable energies, solar PV does not contribute the most to avoiding carbon emissions; nuclear power, wind power, and hydro power avoid even more carbon emissions.##### *Figure 5. Avoided CO2 emission with Electricity generated by Solar PV projection*```{r}#| echo: falsepath_to_sheet <-here("data_raw", "WEO2023_Free_Dataset.xlsx")Ele_proj_data <-partial(.f = read_excel,path = path_to_sheet,sheet ="World Electricity",col_names =FALSE)Ele_STEPS_data <-suppressMessages(Ele_proj_data(range ="M7:Q10"))Ele_NZE_data <-suppressMessages(Ele_proj_data(range ="AM7:AQ10"))Ele_100PV_data <-suppressMessages(Ele_proj_data(range ="AM7:AQ8"))Ele_STEPS_data <-Ele_STEPS_data|>slice(-c(2, 3))Ele_NZE_data <-Ele_NZE_data|>slice(-c(2, 3))Ele_STEPS_data <-t(Ele_STEPS_data )|>as.data.frame()Ele_NZE_data <-t(Ele_NZE_data)|>as.data.frame()Ele_100PV_data <-t(Ele_100PV_data)|>as.data.frame()colnames(Ele_STEPS_data) <-c('year', 'Ele_solarpv')colnames(Ele_NZE_data) <-c('year', 'Ele_solarpv')colnames(Ele_100PV_data) <-c('year', 'Ele_solarpv')Ele_STEPS_data$scenario <-"STEPS"Ele_NZE_data$scenario <-"NZE 2050"Ele_100PV_data$scenario <-"100% Solar Power"combined_Ele<-bind_rows(Ele_STEPS_data, Ele_NZE_data,Ele_100PV_data)combined_Ele$Ele_solarpv<- combined_Ele$Ele_solarpv*0.7716p1 <-ggplot(combined_Ele, aes(x = year, y = Ele_solarpv, color = scenario)) +geom_line()+scale_color_manual(values =c("NZE 2050"="#008000", "STEPS"="red","100% Solar Power"="orange")) +labs(title ="Avoided CO2 emission with Electricity generated by Solar PV project",subtitle ="An assessment on future projections under STEPS vs NZE 2050 vs 100% Solar power scenario",x ="Year",y ="CO2 Emissions avoided(Mt per GW)",color ="Scenario") +theme_minimal(base_size =14) +theme(plot.title =element_text(face ="bold", size =14), plot.subtitle =element_text(face ="italic", size =12), plot.caption =element_text(size =8), axis.title =element_text(size =12),axis.text =element_text(size =12), legend.position ="bottom") +scale_x_continuous(breaks =seq(1900, 2050, by =10)) +scale_y_continuous(labels = scales::comma) interactive_plot <-ggplotly(p1)|>layout(title =list(text ='Avoided CO2 emission by Solar Power<br><sub>An assessment on future projections under STEPS vs NZE 2050 vs 100% Solar power scenario</sub>',x =0.5,xanchor ='center',yanchor ='top' ) )interactive_plot```### 4. Solar PV Supply versus Demand projection**Global solar PV production will reach 2300 gigawatts (by current trend estimate). The range is between 500 gigawatts (conservative estimate) to 8100 gigawawtts (extreme estimate) by 2050.** Using historical data from Statista, we estimate future global PV production trends using an quadratic projection (Figure 6), which is a rational estimate with the current trend. A range is also estimated using a linear model and a cubed model (Figure 7), providing the lower bound and the upper bound of the projected global PV production. Here, the Quadratic model (Figure 6) projects that by 2050, solar PV production will reach 2300 gigawatts annually, which fits well. Figure 7 shows a linear and a cubed projections until 2050, which is simply to provide a range since it is unrealistic to reach both ends. Under this scenario, annual production of Solar PVs will reach about 2300 GW by 2050.Both Figure 6 and Figure 7 show a clear outlier for 2022, visible in the linear and quadratic trends pictured here. This substantial hike in Solar PV supply is likely due to China's overproduction of solar PVs in recent years and signals a This chart confirms that China's production has far surpassed the global demand for solar PVs.##### *Figure 6. Global Solar PV Production from 2000-2050 with Quadratic Projection*```{r}#| echo: false##################### Solar PV production ######################global_PV_production_2000_2022 <-read_excel("data_raw/global-module-manufacturing-production-2000-2022.xlsx", sheet ="Data")global_PV_production_2000_2022 <- global_PV_production_2000_2022%>%rename(year =1, production =2)global_PV_production_2000_2022$year <-as.numeric(as.character(global_PV_production_2000_2022$year))global_PV_production_2000_2022$production <- global_PV_production_2000_2022$production /1000# Convert to gigawattsglobal_PV_production_2000_2022$year <-as.numeric(global_PV_production_2000_2022$year) # Ensure year is numeric############# SOLAR PV LINEAR ############### Fit a linear model to the adjusted datalinear_model <-lm(production ~ year, data = global_PV_production_2000_2022)# Create a dataframe for predictions, from the year after the last year in the dataset to 2050future_years <-data.frame(year =seq(from =max(global_PV_production_2000_2022$year) +1, to =2050, by =1))# Predict future production values using the linear model and add them to the dataframefuture_years$production <-predict(linear_model, newdata = future_years)# Combine the original and predicted datacombined_data <-rbind(global_PV_production_2000_2022, future_years)################# SOLAR PV POLYNOMIAL PRODUCTION #############model <-lm(production ~poly(year, 2), data = global_PV_production_2000_2022)# Create a dataframe for predictions, from the year after the last year in the dataset to 2050future_years <-data.frame(year =seq(from =max(global_PV_production_2000_2022$year) +1, to =2050, by =1))# Predict future production values using the polynomial model and add them to the dataframefuture_years$production <-predict(model, newdata = future_years) # Convert predictions to gigawatts# Combine the original and predicted datacombined_data <-rbind(global_PV_production_2000_2022, future_years)# Create a continuous line data from 2000 to 2050 based on the model for the trend linetrend_data <-data.frame(year =seq(min(global_PV_production_2000_2022$year), to =2050, by =1))trend_data$production <-predict(model, newdata = trend_data) # Convert trend predictions to gigawatts# Find the production value for 2022 and convert it to gigawattsvalue_2022_gw <- global_PV_production_2000_2022 %>%filter(year ==2022) %>%pull(production) # This should already be in gigawatts due to the previous conversion# Plot the data and the projectionp2<-ggplot(combined_data, aes(x = year, y = production)) +geom_col(data = global_PV_production_2000_2022, fill ="darkgreen", show.legend =FALSE) +geom_col(data = future_years, fill ="lightgreen", show.legend =FALSE) +geom_line(data = trend_data, aes(group =1), color ="red", linetype ="dashed") +geom_text(data =subset(combined_data, year ==2022), aes(label = scales::comma(production)), vjust =-0.5, hjust =0.5, color ="black") +labs(title ="Global PV Production",subtitle ="Quadratic projection to 2050",x ="Year",y ="Production (in GW)" ) +theme_minimal(base_size =14) +theme(plot.title =element_text(face ="bold", hjust =0.5),plot.subtitle =element_text(hjust =0.5),axis.title =element_text(face ="bold"),axis.text =element_text(color ="#112446"),axis.text.x =element_text(angle =45, hjust =1),axis.title.x =element_text(vjust =-0.2),axis.title.y =element_text(vjust =2),panel.grid.major.x =element_blank(),panel.grid.minor.x =element_blank() )p2```##### *Figure 7. Global Solar PV Production from 2000-2050 with Linear Projection vs Cubed Projection*```{r}#| echo: false############# SOLAR PV LINEAR ############### Fit a linear model to the adjusted datalinear_model <-lm(production ~ year, data = global_PV_production_2000_2022)# Create a dataframe for predictions, from the year after the last year in the dataset to 2050future_years <-data.frame(year =seq(from =max(global_PV_production_2000_2022$year) +1, to =2050, by =1))# Predict future production values using the linear model and add them to the dataframefuture_years$production <-predict(linear_model, newdata = future_years)# Combine the original and predicted datacombined_data <-rbind(global_PV_production_2000_2022, future_years)# Create a continuous line data from 2000 to 2050 based on the linear model for the trend linetrend_data <-data.frame(year =seq(min(global_PV_production_2000_2022$year), to =2050, by =1))trend_data$production <-predict(linear_model, newdata = trend_data)# Find the production value for 2022value_2022_gw <- global_PV_production_2000_2022 %>%filter(year ==2022) %>%pull(production) # This should already be in gigawatts due to the previous conversion# Plot the data and the projectionp1<-ggplot(combined_data, aes(x = year, y = production)) +geom_col(data = global_PV_production_2000_2022, fill ="darkgreen", show.legend =FALSE) +geom_col(data = future_years, fill ="lightgreen", show.legend =FALSE) +geom_line(data = trend_data, aes(group =1), color ="red", linetype ="dashed") +geom_text(data =subset(combined_data, year ==2022), aes(label = scales::comma(production)), vjust =-0.5, hjust =0.5, color ="black") +labs(title ="Global PV Production",subtitle ="Linear projection to 2050",x ="Year",y ="Production (in gigawatts)" ) +theme_minimal(base_size =14) +theme(plot.title =element_text(face ="bold", hjust =0.5),plot.subtitle =element_text(hjust =0.5),axis.title =element_text(face ="bold"),axis.text =element_text(color ="#112446"),axis.text.x =element_text(angle =45, hjust =1),axis.title.x =element_text(vjust =-0.2),axis.title.y =element_text(vjust =2),panel.grid.major.x =element_blank(),panel.grid.minor.x =element_blank() )################# SOLAR PV cubic PRODUCTION #############model <-lm(production ~poly(year, 3), data = global_PV_production_2000_2022)# Create a dataframe for predictions, from the year after the last year in the dataset to 2050future_years <-data.frame(year =seq(from =max(global_PV_production_2000_2022$year) +1, to =2050, by =1))# Predict future production values using the polynomial model and add them to the dataframefuture_years$production <-predict(model, newdata = future_years) # Convert predictions to gigawatts# Combine the original and predicted datacombined_data <-rbind(global_PV_production_2000_2022, future_years)# Create a continuous line data from 2000 to 2050 based on the model for the trend linetrend_data <-data.frame(year =seq(min(global_PV_production_2000_2022$year), to =2050, by =1))trend_data$production <-predict(model, newdata = trend_data) # Convert trend predictions to gigawatts# Find the production value for 2022 and convert it to gigawattsvalue_2022_gw <- global_PV_production_2000_2022 %>%filter(year ==2022) %>%pull(production) # This should already be in gigawatts due to the previous conversion# Plot the data and the projectionp3<-ggplot(combined_data, aes(x = year, y = production)) +geom_col(data = global_PV_production_2000_2022, fill ="darkgreen", show.legend =FALSE) +geom_col(data = future_years, fill ="lightgreen", show.legend =FALSE) +geom_line(data = trend_data, aes(group =1), color ="red", linetype ="dashed") +geom_text(data =subset(combined_data, year ==2022), aes(label = scales::comma(production)), vjust =-0.5, hjust =0.5, color ="black") +labs(title ="Global PV Production",subtitle ="Cubic projection to 2050",x ="Year",y ="Production (in GW)" ) +theme_minimal(base_size =14) +theme(plot.title =element_text(face ="bold", hjust =0.5),plot.subtitle =element_text(hjust =0.5),axis.title =element_text(face ="bold"),axis.text =element_text(color ="#112446"),axis.text.x =element_text(angle =45, hjust =1),axis.title.x =element_text(vjust =-0.2),axis.title.y =element_text(vjust =2),panel.grid.major.x =element_blank(),panel.grid.minor.x =element_blank() )combined_plot<-p1+p3combined_plot```When comparing supply versus demand (Figure 8.), the graph on the left, titled "Global PV Production Projected to 2050," represents the supply side, showing the annual increase in electricity generated by solar PVs. The graph on the right, titled "Global Annual Increase in Capacity," represents the demand side, illustrating the projected increase in global electricity capacity within the solar PV sector.**Based on our findings from the previous section, if the production of solar PVs increases quadratically, the supply side will be able to meet the maximum scenario demand under the NZE.** This is because the annual increase in electricity capacity demanded by 2050 is around 1800 GW, which is well within the reach of the 2300 GW projected for annual solar PV production increases. In the extreme scenario where 100% of energy comes from solar PVs, the annual increase in electricity capacity demand is expected to reach around 4400 GW. This demand could be met if solar PV production were to follow a cubic growth trend. However, since the 100% solar PV scenario is improbable, solar PV production merely needs to grow quadratically to satisfy future demand.##### *Figure 8. Projected Yearly Increases in Electrical Capacity vs Global PV Production Quadratic Projection*```{r}#| echo: falsecombined_final <- p2+p4combined_final```### ConclusionIn conclusion, current policies are not on track to meet the NZE scenario and greening the energy sector is the key to closing this implementation gap. Solar PVs are an important resource to achieve the rapid carbon reduction. We found that solely by reaching 100% Solar power by 2050 or the NZE scenario solar capacity, we can reduce enough emissions to get very close to or beyond net zero. Furthermore, we found a quadratic increase in solar PV production will help us reach the NZE and STEPS levels of Solar PV demand and a cubic increase is enough to set us on track for even the full solar scenario.**\References:**Business Insider (2024). <https://markets.businessinsider.com/news/commodities/solar-panel-oversupply-prices-iea-renewables-production-green-transitions-inventory-2024-1>Financial Times (2024). <https://www.ft.com/content/2ea6bf6d-04e9-453b-a35f-cd6431cfc7bf>International Energy Agency (2020). <https://www.iea.org/data-and-statistics/charts/annual-direct-co2-emissions-avoided-per-1-gw-of-installed-capacity-by-technology-and-displaced-fuel>Statista, (2023). Annual solar module production globally from 2000 to 2022. <https://www.statista.com/statistics/668764/annual-solar-module-manufacturing-globally/>UN (2024). <https://www.un.org/en/climatechange/net-zero-coalition#:~:text=Yes%2C%20a%20growing%20coalition%20of,about%2088%25%20of%20global%20emissions.>World Population Review (2024). <https://worldpopulationreview.com/country-rankings/solar-power-by-country>
