Visualizing the current standing of CO2e’s in Taiwan

Understanding Taiwan’s emissions trends through emission per capita, contributing fuel sources, and sector-specific emissions
Author

Flora Hamilton

Published

March 12, 2024

In this blog post, I’ll be diving into the current carbon emissions trends of Taiwan, and visualizing these trends with three plot types. Taiwan is an island located in the western Pacific Ocean, about 100 miles off the coast of southeastern China. It’s approximately 245 miles long and 90 miles wide and is home to about 24 million people who speak languages like Mandarin, Hokkien, Hakka, and indigenous languages.

Why Taiwan?

I’ve chosen to analyze Taiwan’s carbon emissions because it is a country that has enormous potential to decarbonize. Taiwan’s biggest power station is also the world’s #1 top emitter of GHG emissions. According to Statista, “The Taichung Power Plant in Taiwan was the most polluting power station worldwide in 2022, having released an estimated 32.7 million metric tons of carbon dioxide equivalent (MtCO₂e). With an installed capacity of 5,500 megawatts, the coal-fired Taichung Power Plant is the fourth largest power station in the world.” Just decarbonizing this one power plant alone can result in a dramatic reduction in Taiwan’s overall emissions.

Transitioning to renewable energy is crucial for Taiwan. By moving towards renewable energy sources, Taiwan can enhance its energy security by reducing dependence on imported fossil fuels. Embracing renewables also helps in combating climate change and reducing greenhouse gas emissions, aligning with global sustainability efforts. Economically, investing in clean energy offers growth opportunities and innovation potential, with Taiwan’s private sector already showing commitment to a cleaner energy future. This shift not only positions Taiwan as a regional leader in sustainable energy but also contributes to an environmentally resilient future for the island. We will be getting a better of idea of where Taiwan currently stands in its emissions generation through visualizing data from ClimateTrace and OurWorldInData.

The infographic

Data, Approach, Limitations

The Data

  • Sector specific emissions: This year ClimateTrace introduced an open emissions database with over 352 million assets, covering human-caused emissions from various facilities globally. It spans every country, major economic sector, and nearly all greenhouse gas emission sources. This detailed dataset, accessible for free, is one of the most comprehensive and granular view of recent GHG emissions available for the public. Utilizing satellites, remote sensing, AI, and machine learning, it offers transparency for assessing each country’s emission reduction progress. This project subsets the dataset for Taiwan’s emissions from 2016-2022.

    Access the dataset here.

  • Historical CO2 per capita emissions & CO2 contributions by fuel type:

    OurWorldInData compiles data on many countries’ emissions data from work from many different people and organizations. We will be using their datasets historical CO2 per capita emissions & CO2 contributions by fuel type in 2020, subsetting for just Taiwan’s data.

    Access the datasets here.

The Approach


I chose to communicate the data through an infographic, incorporating a line plot, pie chart, and stacked area chart arranged to evoke an industrial ambiance. The line chart mimics the emission of smoke from a stack, the pie chart suggests a moon, and the stacked area chart resembles a container on a ship. The overall dark, nocturnal theme not only adds a sense of foreboding but also implies a lack of transparency, creating an ominous atmosphere for the observer. A keen-eyed viewer might notice an additional smoke plume subtly resembling the shape of Taiwan.

For enhanced visibility, I opted for high-contrast colors of dark grey and white, and bolded the text in Josefin and Times New Roman for the title. Alt text was included to improve web usability. To prevent information overload, I balanced the data-ink ratio, removing grid lines and simplifying the message, especially in the stacked area plot, where font size indicates relative proportions.

The predominantly monochrome palette ensures color-blind friendliness, and I used online colorblind simulators to confirm the graphic’s accessibility. To address the topic on level of individual agency, I incorporated a graphic detailing Taiwan’s per capita emissions. Additionally, I provided a concise blurb to contextualize the significance of the topic, ensuring that viewers grasp its importance.

Limitations

This visualization falls short of directly engaging with the critical aspects surrounding the feasibility of decarbonization in Taiwan. It does not delve into the economic, socioeconomic, and environmental repercussions associated with such a transition. Furthermore, it does not offer actionable suggestions. Instead, the visualization primarily serves as an overview of Taiwan’s current carbon emissions landscape.

Let’s visualize!

Setting up Libraries, loading font types

Code 
# --------------------------------------------
#  Setup libraries 
# --------------------------------------------

library(tidyverse)
library(ggplot2)
library(treemapify)
library(ggrepel)
library(showtext)
library(extrafont)
library(ggtext)
library(patchwork)
library(glue)

#..........................import fonts..........................
font_add_google(name = "Josefin Sans", family = "josefin")
font_add_google(name = "Sen", family = "sen")

#................enable {showtext} for rendering.................
showtext_auto()

Plot 1: Stacked Area Plot of GHG emissions by sector type, in Taiwan (2016-2022)

Data Cleaning

ClimateTrace’s dataset is large, and we can aggregate sectors into subsectors, based on ClimateTrace’s grouping conventions.

Code 
# --------------------------------------------
# Loading/Cleaning Climate Trace emissions data
# --------------------------------------------
emissions <- read.csv("~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/data/trace_data_since_2015_to_2022_countries_TWN.csv")


# Data cleaning & rename each subsector
emissions_df <- emissions %>% 
  select(sector, year, co2e_100yr) %>% 
  mutate(subsector = case_when(
    sector %in% c("solid-waste-disposal", 
                  "biological-treatment-of-solid-waste-and-biogenic", 
                  "wastewater-treatment-and-discharge",
                  "incineration-and-open-burning-of-waste") ~ 'waste',
    
    sector %in% c("manure-left-on-pasture-cattle", 
                  "forest-land-fires", 
                  "rice-cultivation", 
                  "enteric-fermentation-cattle-pasture", 
                  "manure-management-cattle-feedlot", 
                  "enteric-fermentation-cattle-feedlot", 
                  "other-agricultural-soil-emissions", 
                  "enteric-fermentation-other",  
                  "cropland-fires", 
                  "shrubgrass-fires", 
                  "manure-management-other") ~ "agriculture", 
    
    sector %in% c("other-onsite-fuel-usage", "residential-and-commercial-onsite-fuel-usage") ~ "buildings", 
    
    sector %in% c("fluorinated-gases") ~ "fluorinated gases",
    
    sector %in% c("oil-and-gas-production-and-transport",
                  "oil-and-gas-refining", 
                  "other-fossil-fuel-operations") ~ "fossil fuel operations", 
    
    sector %in% c("water-reservoirs", 
                  "net-forest-land", 
                  "forest-land-clearing", 
                  "forest-land-degradation",
                  "net-wetland", 
                  "net-shrubgrass", 
                  "wetland-fires") ~ "forestry and land use", 
    
    sector %in% c("aluminum",
                  "other-manufacturing",  
                  "chemicals",
                  "cement", 
                  "pulp-and-paper",
                  "petrochemicals",
                  "steel") ~ "manufacturing", 
    
    sector %in% c("copper-mining",
                  "iron-mining", 
                  "sand-quarrying", 
                  "rock-quarrying") ~ "mineral extraction", 
        

    sector %in% c("electricity-generation",  
                  "other-energy-use") ~ "power", 
    
    sector %in% c("international-shipping",  
                  "domestic-aviation", 
                  "domestic-shipping", 
                  "international-aviation",
                  "other-transport", 
                  "railways", 
                  "road-transportation") ~ "transportation", 
    
    sector %in% c("removals") ~ "removals"
  ))

# Filter out rows based on multiple values
values_to_exclude <- c( "biological-treatment-of-solid-waste-and-biogenic", "other-transport", "removals")
emissions_df <- emissions_df %>% drop_na() %>% filter(!(sector %in% values_to_exclude))


### Now, aggregate emissions for each subsector 
emissions_df_subsector <- emissions_df %>% group_by(subsector, year) %>% mutate(co2e_100yr_subsector = sum(co2e_100yr)) %>% select(-c("co2e_100yr", "sector"))

The next step is to ignore negative emissions, or CO2e sinks, so that we can analyze just total emissions output (as opposed to net emissions). Taiwan’s negative emissions are quite small, so it would simplify this analysis a bit to remove it.

Code 
###for emissions by gas type... removing sinks / removals in order to show totals not net. 
emissions_gas <- emissions %>% select(year, co2, ch4, n2o) %>% 
  group_by(year) %>% 
  summarise(
    co2 = sum(co2, na.rm = TRUE),
    ch4 = sum(ch4, na.rm = TRUE),
    n2o = sum(n2o, na.rm = TRUE)
  ) %>% pivot_longer(cols = co2:n2o,
                     names_to = "gas",
                     values_to = "annual_emissions")

emissions_sorted <- emissions_df %>%
  group_by(sector) %>%
  arrange(year)

Plotting

Code 
#.......................wrangling and subsetting data.........................

emissions_df_subsector_unique <- emissions_df_subsector[!duplicated(emissions_df_subsector), ]
emissions_df_subsector_unique$subsector<- factor(emissions_df_subsector_unique$subsector , levels=c("power","manufacturing","transportation","fossil fuel operations", "waste" , "fluorinated gases", "buildings", "agriculture", "mineral extraction", "forestry and land use") )


#.......................setting colors for graph ............................

mycol <- c("red4", "#C5936A", "green4", "purple4", "grey", "grey50", "grey45", "grey40","grey35", "grey20")


#......................assembling stacked area plot .......................

stacked_area <- emissions_df_subsector_unique %>% 
  ggplot(aes(x = year, y = co2e_100yr_subsector, fill = subsector)) +
  geom_area(alpha = 0.7, show.legend = FALSE) +
  labs(x = "Year", y = "CO2e (100yr) in Megatons (MT)", alt = "Stacked area plot showing that the power sector is the largest contributing sector to Taiwan's GHG Emissions from 2015-2022, followed by manufacturing and transportation. Emissions are represented in billions of Megatons at a 100yr time horizon.") +
  theme_minimal() +
  ggtitle("Power Sector Dominates Taiwan's GHG Emissions (2015-2022)") +
  scale_y_continuous(labels = scales::label_scientific()) +
  scale_fill_manual(values = mycol, name = "Sectors") +  # Use scale_fill_discrete for fill color
  theme(panel.grid = element_blank(),
    text = element_text(family = "sans", size = 20), 
    plot.title = element_text(color = "white", size = 40, family = "josefin",
                              face = "bold"), 
    plot.caption = element_text(size = 20, color = "white"),
    strip.background = element_rect(fill = "black", color = "black"),  
        panel.background = element_rect(color = "grey20", fill = "grey20"), 
        plot.background = element_rect(color = "grey20", fill = "grey20"), 
        axis.text = element_text(color = "white", family = "josefin", size = 30),  
        axis.text.y = element_text(color = "white", margin = margin(t = 2), size = 30),  
        axis.title = element_text(color = "white", family = "josefin", size = 35), 
      legend.text = element_text(color = "white", size = 20), 
      legend.title = element_text(color = "white", size = 20), 
      legend.position = "bottom", 
      plot.margin = margin(t = 5,r = 12, b=20, l=10)) + 
  geom_text(x = 2021, y = max(emissions_df_subsector_unique$co2e_100yr_subsector)*1.5 , label = "Power", vjust = .5, hjust = 0.5, color = "pink3", size = 40, family = "josefin",face = "bold") +
   geom_text(x = 2021, y = max(emissions_df_subsector_unique$co2e_100yr_subsector)*0.82 , label = "Manufacturing", vjust = .5, hjust = 0.5, color = "tan", size = 15, family = "josefin", face = "bold") + 
   geom_text(x = 2021, y = max(emissions_df_subsector_unique$co2e_100yr_subsector)*0.52 , label = "Transportation", vjust = .5, hjust = 0.5, color = "#a6b58f", size = 15, family = "josefin", face = "bold") + 
   geom_text(x = 2021, y = max(emissions_df_subsector_unique$co2e_100yr_subsector)*0.28 , label = "Fossil fuel operations", vjust = .5, hjust = 0.5, family="josefin", face = "bold", color = "#b39ae4", size = 9) + 
   geom_text(x = 2021.3, y = max(emissions_df_subsector_unique$co2e_100yr_subsector)*0.13 , label = "Other", vjust = .5, hjust = 0.5, color = "grey75", family="josefin", face = "bold", fontface="italic",  size = 10)+ 
  geom_rect(xmin = 2021.9,xmax = 2022, ymin = max(emissions_df_subsector_unique$co2e_100yr_subsector)*0.01, ymax = max(emissions_df_subsector_unique$co2e_100yr_subsector)*0.23, fill = "grey87", alpha = 0.0082)+ 
    scale_y_continuous(expand = c(0, 0)) + 
  scale_x_continuous(expand = c(0, 0))

 stacked_area <- stacked_area + scale_y_continuous(labels = function(x) format(x / 1e8, scientific = FALSE, big.mark = ","),  name = "CO2e (100yr) in Billions of MT")
 
ggsave(plot = stacked_area, filename = "~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/final_work/stacked_area.png", height = 10, width = 16)

knitr::include_graphics("~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/final_work/stacked_area.png")

This stacked area plot investigates emissions by sector, showing each sector’s relative proportion of co2 emissions in billions of megatons from 2015 to 2022, as well as the total emissions in billions of megatons. The choice of a stacked area plot was motivated by the consistent stability of overall emissions across the years within each sector; notably, the power sector occupies the greatest proportion, followed by manufacturing, transportation, and fossil fuel operations. To enhance the clarity, I arranged the proportions by size, grouping those smaller than fossil fuel operations into the “other” category, colored in a grey scale. To simplify the message and reduce information overload, I opted to emphasize relative contribution through Font size, rather than percentage. Although the font size is not tied to any strict proportion, it effectively communicates that power holds the largest share of contributions.

Plot 2: Pie chart of emissions contributions by sector (2022)

Code 
#................load data and select relevent column & format  .........................
co2_power_brkdown2022 <- read.csv("~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/data/co2-by-source_World_in_Data.csv") %>% filter(Entity == "Taiwan", Year == 2022 ) %>% select(-c("Code", "Annual.CO..emissions.from.cement", "Annual.CO..emissions.from.flaring"))

co2_power_brkdown2022_long <- pivot_longer(co2_power_brkdown2022, 
                        cols = starts_with("Annual.CO..emissions.from."), 
                        names_to = "Power_Type", 
                        values_to = "Emissions")

custom_colors <- c("lightyellow", "grey","#91a3b0","goldenrod3")  # Add more colors if needed

#................plot.....................................................


# create pie chart base
pie_chart <- ggplot(co2_power_brkdown2022_long, aes(x = "", y = Emissions, fill = Power_Type)) +
  geom_bar(stat = "identity", width = 1, color = "white") +
  coord_polar(theta = "y") +
  labs(
    title = "Coal: Primary Contributor to Taiwan's \nPower Sector Emissions (2022)",
    fill = "Fuel Type",
    y = "Emissions", alt = "Pie chart showing that coal is the primary contributor to Taiwan's power secotr emissions in 2022 (55.9%), followed by oil and gas equally at ~22% each."
  ) +
  
  ##add labels 
  geom_label(
    aes(x = ifelse(Emissions == min(Emissions), 1.7, 1.15),
        label = paste0(round(Emissions / sum(Emissions) * 100, 1), "%")),
    position = position_stack(vjust = 0.5), size = 10, color = "grey20",
    fill = rev(custom_colors), face = "bold", show.legend = FALSE
  ) +  # Background behind percentage labels

  geom_text(
    aes(x = 1.75, label = c("", "Gas", "Oil", "Coal")),
    position = position_stack(vjust = 0.5), size = 15, color = "white", family = "josefin"
  ) +

  geom_text(
    aes(x = 1.9, y = 0, label = "Other"),
    size = 10, color = "goldenrod3", family = "josefin"
  ) +

  scale_fill_manual(values = custom_colors) +
  theme_minimal() +
  theme(
    plot.margin = margin(0.2, 0, -1.5, 0, "cm"),
    axis.text = element_text(color = "grey20"),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(),
    legend.position = "none",
    strip.background = element_rect(fill = "grey20", color = "grey20"),
    panel.background = element_rect(color = "grey20", fill = "grey20"),
    plot.background = element_rect(color = "grey20", fill = "grey20"),
    plot.title = element_text(
      color = "white", size = 45, family = "josefin",
      face = "bold", lineheight = 0.3
    ),
    plot.subtitle = element_text(color = "white")
  )

# Include the image
knitr::include_graphics("~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/final_work/final_pie.png")

This pie plot extends upon the stacked area plot, to ask the further question, within the power sector, what fuel source is the largest emitter of green house gas? This pie plot clearly shows the contributions of coal, oil, and gas, along with an “others” category to overall power emissions. Coal claims the largest segment, comprising approximately 56% of the total, while oil and gas evenly divide the remaining portion, each contributing around 22%. Once again, to maintain clarity and avoid information overlaoad, I’ve chosen to streamline the information by emphasizing the percentage contributions rather than presenting the absolute quantity.

Plot 3: Line plot of Taiwan’s Per Capita Emissions

Code 
#..........load data and select relevent columns & timeframe .......................

co2_percapita_brkdown2022 <- read.csv("~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/data/co-emissions-per-capita.csv") %>% 
  filter(Entity %in% c( "Taiwan", "United States", "China", "High-income countries", "Upper-middle-income countries", "Lower-middle-income countries", "Low-income countries")) %>% 
  filter(Year > 1825) %>% 
  rename(co2e_per_capita_yr = Annual.CO..emissions..per.capita., year = Year) %>% 
  select(-Code) 

# Manually set colors for each line
line_colors <- c("white", "grey60", "grey60", "grey60", "gold", "white", "grey60")


#..............create plot.................

line_plot <- ggplot(data = co2_percapita_brkdown2022, aes(x = year, y = co2e_per_capita_yr, group = Entity, color = Entity)) + 
  geom_line(aes(color = Entity), show.legend = FALSE) +  
  scale_color_manual(values = line_colors) + 

  labs(title = "Taiwan's Per Capita Emissions Surpass Most High-Income Nations",
       x = "Year",
       y = "Taiwan's CO2 Emissions Per Capita (Tons)", 
       alt = "Line plot illustrating the per capita emissions of Taiwan compared to the US, China, and global averages by income status from the 1830s to 2022, measured in Tons. The plot visually emphasizes Taiwan's higher per capita emissions, surpassing the majority of high-income nations over the specified time period.") +
    
  geom_text(data = co2_percapita_brkdown2022 %>% filter(Entity == Entity),
            aes(label = ifelse(year == max(year), Entity, "")),
            hjust = 0, 
            position = position_nudge(x= 3), 
            size = 12) +
  
  coord_cartesian(clip = 'off', ylim = c(0, NA)) +
  
  theme(legend.position = 'none',
        plot.margin = margin(0.5, 5, 0.5, 0.5, "cm"), 
        panel.grid.minor = element_blank(), 
        panel.grid.major = element_blank(), 
        strip.background = element_rect(fill = "grey20", color = "grey20"),  
        panel.background = element_rect(color = "grey20", fill = "grey20"), 
        plot.background = element_rect(color = "grey20", fill = "grey20"),
        
        axis.text = element_text(color = "white", size = 30, family = "josefin"),  
        axis.title = element_text(color = "white", family = "josefin", size = 30),  
        plot.title = element_text(color = "white", size = 40, family = "josefin", face = "bold"),  
        axis.title.x = element_text(margin = margin(2, 0, 0, 0)),
        plot.subtitle = element_text(color = "white")
  ) + 
  scale_y_continuous(expand = c(0, 0))

knitr::include_graphics("~/Documents/2_Areas/Areas_MEDS/Winter_Quarter/Data_Viz_240/HWs/hamilton-eds240-HW4/final_work/final_line.png")


I use a line plot to address the question: “How does Taiwan’s per capita emissions stack up against those of other nations globally?” The visual representation juxtaposes Taiwan’s per capita emissions with those of the United States and China, alongside averages for country groups based on income. Taiwan is highlighted with a gold color, while the United States and China are in white. The income group averages are grey. This dataset spans from the onset of the industrial revolution (early 1800’s), and it shows a surge in Taiwan’s emissions post-1960, coinciding with Taiwan’s robust economic expansion during that period.

In the 1960s, Taiwan experienced the “Taiwan Miracle,” marked by rapid economic growth and industrialization. Key factors included land reforms, export-oriented policies, foreign investments, and educational advancements. This period shifted Taiwan’s economy from that based on agriculture to export-focused industries, resulting in significant economic expansion and a surge in per capita emissions post-1960.

Notably, we see that Taiwan’s emissions are now higher than that of most high-income countries, including China, and are converging close to that of the United States!

Assembling an Infographic

Code 
# 🔡 text ---------------------------------------------------
quotes <- c("Taiwan's current carbon emissions standing is significant, with the country being among the top 25 largest carbon emitters globally. In recent years, Taiwan has been taking steps towards reducing its carbon footprint and transitioning to cleaner energy sources to achieve net-zero emissions by 2050. The country has set ambitious goals, such as investing in renewable energy technologies and implementing a carbon fee system for large emitters to incentivize emission reduction. In order to understand how these plans can change Taiwan’s carbon footprint in the long-term, we need to first understand the country’s current baseline in terms of carbon emissions. 
", 
            "The country's CO2 emissions per capita have also varied, with Taiwan emitting around 11 to 12 tons of CO2 per person annually. Taiwan’s total CO2 emissions current fluctuate around 300 million tons a year, almost half of which is from power generation. Coal is the dirtiest power source, contributing over 55% of the CO2 emissions from the power sector. 
")
quote1<-ggplot() +
  annotate("text", x = 0, y = 1, label = str_wrap(quotes[1], 100),
           family = "josefin", colour = "white", size = 16, hjust = 0, lineheight = 0.3) +
  xlim(0, 1) +
  ylim(0, 1) +
  theme_void() +
  coord_cartesian(clip = "off")

quote2<-ggplot() +
  annotate("text", x = 0, y = 1, label = str_wrap(quotes[2], 50),
           family = "josefin", colour = "white", size = 15, hjust = 0,  lineheight = 0.25) +
  xlim(0, 1) +
  ylim(0, 1) +
  theme_void() +
  coord_cartesian(clip = "off")



# 📊 set up the base chart
g_base <- ggplot() +
  labs(
    title = "What is the current standing of carbon emissions in Taiwan?", 
    alt = "Infographic illustrating Taiwan's carbon emissions status through a line plot, pie chart, and stacked area plot. The line chart underscores Taiwan's elevated per capita emissions, surpassing that of many high-income nations. The pie chart and stacked area plot explore the dominating role of the power sector, revealing coal as the predominant contributor among various fuel sources."
    ) +
  theme_void() +
  theme(
    text = element_text(family = "josefin", size = 12, lineheight = 0.3, colour = "white"),
    plot.background = element_rect(fill = "grey20", colour = "grey20"),
    plot.title = element_text(size = 120, face = "bold", hjust = 0, margin = margin(b = 10), family = "serif", lineheight=0.3),
    plot.subtitle = element_text(family = "serif", hjust = 0, margin = margin(b = 20), size = 90),
    plot.caption = element_markdown(family = "josefin", colour = "white", hjust = 0.5, margin = margin(t = 20)),
    plot.margin = margin(b = 20, t = 50, r = 50, l = 50),
    axis.text.x = element_text()
  )


# final chart
g_final <- g_base +
  inset_element(pie_chart, left = 0.7, right = 1, top = 1, bottom = 0.55) +
  inset_element(line_plot, left = 0.06, right = 0.65, top = 0.75, bottom = 0.35) +
  inset_element(stacked_area, left = 0.20, right = 0.8, top = 0.37, bottom = 0)  +
  inset_element(quote1, left = 0.05, right = 0.6, top = 0.9, bottom = 0.8) +
  inset_element(quote2, left = 0.7, right = 1, top = 0.48, bottom = 0.43)+
  plot_annotation(
    theme = theme(
      plot.background = element_rect(fill = "grey20", colour = "grey20") 
      
    )
  )

This is pretty close to being finished! I wanted to add a cityscapes background, a ship, and some smoke stacks, along with a png file of a plume in the shape of Taiwan. I continued the work in Google Slides to create the desired effects (cutting and pasting a bit, to nudge objects to where they needed to be) and the final result is below.

Key Insights:

Dominance of Power Sector Emissions:

  • Power sector consistently emerges as the major contributor to CO2 emissions, with manufacturing, transportation, and fossil fuel operations following in decreasing order.

Coal’s Significant Role in Power Emissions:

  • Coal stands out as the primary emitter in the power sector, constituting approximately 56% of total emissions, while oil and gas evenly share the remaining portion.

Taiwan’s Surging Per Capita Emissions:

  • Post-1960, Taiwan experiences a notable increase in per capita emissions, surpassing those of most high-income countries and approaching levels seen in the US.
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