The path to 1.5 Celsius

2025

Robbie Andrew’s mitigation curves required to meet the Paris Agreement’s 1.5°C and 2°C targets.

Author

Affiliation

Guillermo De-Taranco-López

 

Published

Jan. 13, 2026

Citation

De-Taranco-López, 2026

Introduction

The 1.5°C global warming limit was established as a key goal within the 2015 Paris Agreement, an international treaty adopted at the UN Climate Change Conference (COP21) in Paris, France. The agreement aimed to hold temperature increases “well below 2°C above pre-industrial levels and pursue efforts to limit the temperature increase to 1.5°C”.

This project explores the visualization of this critical threshold, analyzing historical data and mitigation curves to understand the urgency of the situation.

Original graph

This graph was made originally by Robbie Andrew, a senior researcher at the Center for International Climate Research (CICERO). It plots CO2 emissions in Gigatonnes of CO2 between the yeas of 1750 and 2019 mapping the year to the x-axis and CO2 emissions to the y-axis. One Gigatonne of Carbon Dioxide equals it’s 1 trillion kilograms. The source specifies that the sources of the CO2 are fossil fuels and land-use change.

It also plots mitigation lines. These are mathematical trajectories that visualize the required annual reduction in global emissions to stay within a specific carbon budget.

Before we start…

Libraries

library(tidyverse)
library(RColorBrewer) # Helpful for generating standard palettes
library(ggtext)
library(patchwork)
library(grid)
library(gridExtra)

Load the data

The data used to replicate this graph was found in the personal website of Robbie Andrew. It is publicly accessible online although it’s slightly outdated

The dataset are downloadable here: 1.5C, 2C.

We use a function to get the data in the correct long format:

# Define a function to process the files
process_emissions_data <- function(filename) {
  read_csv(filename) |>
    pivot_longer(
      cols = -Year,
      names_to = "Scenario",
      values_to = "Emissions"
    ) |>
    filter(!is.na(Emissions)) |>
    mutate(
      StartYear = as.numeric(Scenario),
      Type = ifelse(Scenario == "Historical", "History", "Mitigation")
    )
}

# Load both datasets in one go
df_plot    <- process_emissions_data("mitigation_curves_1.5C_191203_data.csv")
df_plot_2C <- process_emissions_data("mitigation_curves_2.0C_191203_data.csv")

Replicating the graph

Theme

Before building the plot, we define the visual theme. This ensures consistency across the visualization, removing unnecessary background elements to focus on the data.

my_theme <- function() {
  theme_minimal(base_family = "sans", base_size = 14) +
  theme(
    # MAIN LAYOUT & MARGINS --------------------------------------------------------
    # Adds whitespace on all sides
    plot.margin = margin(t = 0.9, r = 2.7, b = 1.5, l = 0.8, unit = "cm"),

    # Backgrounds
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "white", color = NA),

    # Grid lines
    panel.grid.major.y = element_line(color = "grey90", size = 0.5),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),

    # Axes
    axis.line.x = element_line(color = "grey50", size = 0.5),
    axis.line.y = element_line(color = "grey50", size = 0.5),
    axis.ticks = element_line(color = "grey50", size = 0.5),
    axis.ticks.length = unit(0.2, "cm"),

    # Text & Labels
    text = element_text(color = "grey30"),
    axis.text = element_text(color = "grey30", size = 22),
    axis.text.y = element_text(vjust = 0.5),
    
    axis.title = element_blank(),

    # Legend
    legend.position = "none",

    # TITLE STYLING
    plot.title = element_text(
      face = "bold",      
      size = 38,              # Large font (approx 18-20pt)
      color = "black",        #pure black
      hjust = 0,  
      margin = margin(b = 8)  # Add small space between Title and Subtitle
    ),

    # SUBTITLE STYLING
    plot.subtitle = element_text(
      face = "plain",         # Regular weight
      size = 22,              # Smaller than title, legible
      color = "black",        # Dark text
      lineheight = 0.5,       # specific spacing between the subtitle lines
    ),
  )
}

Historical data

First we plot the basic line that is plotted in both sides of the graph. This black line represents historical emissions.

historical<- ggplot() +
    # FIX: Filter the long data for "History" and use the 'Emissions' column
    geom_line(data = df_plot |> filter(Type == "History"), 
              aes(x = Year, y = Emissions), 
              color = "black", linewidth = 1.2) + # Thickest linewidth
  my_theme()


historical

Left plot

Mitigation curves

The data for this graph was made in 2019. So they focus with a grey line the mitigation required to stick to the carbon budget for that year. For mitigation lines before that year, the lines appear blue, for mitigation lines after, they appear red.

We create a code that can be used to automatically label the data for Past, Current and Future lines to color them later

focus_year <- 2019 

df_plot <- df_plot |>
  mutate(
    TimePeriod = case_when(
      StartYear < focus_year ~ "Past",
      StartYear == focus_year ~ "Current",
      StartYear > focus_year ~ "Future"
    ),
    IsFocusYear = (StartYear == focus_year)
  )

To color this lines we create a palette with as many blue colors needed (dark to light) and as many red colors (light to dark). This creates a palette for the left plot but not the right since they have a different number of mitigation lines.

# Counts how many years exist in each period
years_past   <- n_distinct(df_plot$StartYear[df_plot$TimePeriod == "Past"])
years_future <- n_distinct(df_plot$StartYear[df_plot$TimePeriod == "Future"])

# Generate the gradients dynamically using those counts
blues_gradient <- colorRampPalette(c("#003366", "#d1e5f0"))(years_past) 
reds_gradient  <- colorRampPalette(c("#fddbc7", "#f3513a"))(years_future)
grey_highlight <- "#6a6a6a"

# Stitch them together
final_palette <- c(blues_gradient, grey_highlight, reds_gradient)

Finally we plot the lines along with the proper colors and sizes:

p <- historical +

  # MITIGATION LINES-----------------------------------------------------------------  
  
  geom_line(
    data = df_plot |> filter(Type == "Mitigation"), 
    aes(
      x = Year, 
      y = Emissions, 
      group = StartYear, 
      color = as.factor(StartYear),
      size = IsFocusYear 
    ), 
    alpha = 0.9
  )+
  
  # COLORS ---------------------------------------------------------------------------

  scale_color_manual(values = final_palette) +
  
  # LINE WIDTHS ----------------------------------------------------------------------

  # Maps the "IsFocusYear" (TRUE/FALSE) to specific thicknesses
  scale_size_manual(
    values = c("FALSE" = 0.5, "TRUE" = 1), # False=Others, True=2019
    guide = "none" # This hides the legend for line thickness
  ) +

  # SCALES --------------------------------------------------------------------------
  scale_x_continuous(
    breaks = c(1980, 2000, 2020, 2040, 2060, 2080, 2100),
    limits = c(1980, 2101),
    expand = expansion(0)
  ) +
  scale_y_continuous(
    breaks = c(0, 10, 20, 30, 40),
    labels = c("0", "10", "20", "30", ""), 
    expand = expansion(mult = c(0, 0.05))
  )

p

Annotations

First create the poligons (the blue boxes). Then I add the white text on top of them and the rest of the annotations.

# --- we define the Polygon Shape counter-clockwise order to ensure the shape isn't twisted.
#Tip -> Wedge Top -> Box Top Left -> Box Top Right -> 
#->Box Bottom Right -> Box Bottom Left -> Wedge Bottom


poly_df <- data.frame(
        #tip    wedt    topl    topr   botr   botl  wedb
  x = c( 2026,  2032,  2032, 2066.5,2066.5,  2032,  2032), 
  y = c(42.33,    41, 42.55,  42.55,    33,    33,    39) 
)

poly_df2 <- data.frame(
        #tip    wedt    topl  topr    botr   botl  wedb
  x = c( 2024,  2030,   2030, 2071,  2071,  2030,  2030), 
  y = c(27.22,    26,     28,   28,    20,    20,    25) 
)

poly_df3 <- data.frame(
        #tip    wedb  botr    topr     topl  botl   wedt              
  x = c( 2064,  2066, 2095,   2095,    2063, 2063, 2064), 
  y = c(3.301,     5,    5,   16.5,    16.5,    5,    5) 
)
p <- p +

  # TITTLE AND SUBTTILES----------------------------------------------------------------

  labs(
    title = "The path to 1.5°C has become a cliff edge...",
    subtitle = "Each line shows the trajectory of annual emissions cuts required for\na greater than 50 per cent chance of staying below 1.5°C,\ndepending on the year that emissions peak"
  ) +

  # GIGATONS OF CO2--------------------------------------------------------------------

  annotate("text", x = 1980, y = 40, label = "40 Gt", hjust = 1.4, vjust = -0.5, size=7, color = "grey30") +
  annotate("text", x = 1980, y = 40, label = "CO[2]", parse = TRUE, hjust = 1.5, vjust = 1, size=7, color = "grey30") +

  # SOURCE-----------------------------------------------------------------------------

  annotate("text", x = 1980, y = 0, label = "Source: Robbie Andrews/Data:GCP/Emissions budget from IPCC AR6", hjust = 0.04, vjust = 6, color = "grey50", size=7.5) +
  coord_cartesian(xlim = c(1980, 2100), ylim = c(-0.1, 44), clip = "off") +

  # POLIGONS-----------------------------------------------------------------------------

  # The Faint Grey Line that connects 2019 (graph peak) to 2026 (wedge tip)
  annotate("segment",x = 2019, xend = 2026,y = 42.33, yend = 42.33,
           color = "grey70",size = 0.5) +
  geom_polygon(data = poly_df,aes(x = x, y = y),fill = "#0055AA",color = NA) +
  geom_polygon(data = poly_df2,aes(x = x, y = y),fill = "#0055AA",color = NA) +
  geom_polygon(data = poly_df3,aes(x = x, y = y),fill = "#0055AA",color = NA) +

  # WHITE TEXT 1 ------------------------------------------------------------------------

  annotate("text",x = 2034,y = 40.6,label = "Even keeping emissions",hjust = 0,vjust = 0, color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2034,y = 38.4,label = "constant for 10 years",hjust = 0,vjust = 0, color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2034,y = 36.1,label = "will use up the remaining",hjust = 0,vjust = 0, color = "white",size = 5.45,lineheight = 1.0)  +
  annotate("text",x = 2034,y = 33.8,label = "carbon budget",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0)+

  # WHITE TEXT 2 ------------------------------------------------------------------------

  annotate("text",x = 2031.5,y = 25.6,label = "If we start cutting",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2031.5,y = 23.3,label = "emissions today, we must",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2031.5,y = 21.1,label = "do so rapidly every year",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0)+

  # WHITE TEXT 3 ------------------------------------------------------------------------

  annotate("text",x = 2064.5,y = 14,label = "If we had started in",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2064.5,y = 11.6,label = "2000 we would only",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2064.5,y = 9.2,label = "have needed to cut by",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2064.5,y = 6.8,label = "3 per cent each year",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0)

p

Right plot

We follow the same framework as with the right plot

Mitigation curves

# Create helper columns so the plot code reads like English
df_plot_2C <- df_plot_2C |>
  mutate(
    # Create a clear category for coloring logic
    TimePeriod = case_when(
      StartYear < focus_year ~ "Past",
      StartYear == focus_year ~ "Current",
      StartYear > focus_year ~ "Future"
    ),
    # Create a logical column for line thickness
    IsFocusYear = (StartYear == focus_year) # Focus year is the same on both plots
  )
# 1. Count exactly how many years exist in each period
# This makes the code robust: if you add more data, the colors update automatically.
years_past_2C   <- n_distinct(df_plot_2C$StartYear[df_plot_2C$TimePeriod == "Past"])
years_future_2C <- n_distinct(df_plot_2C$StartYear[df_plot_2C$TimePeriod == "Future"])

# 2. Generate the gradients dynamically using those counts
blues_gradient_2C <- colorRampPalette(c("#003366", "#d1e5f0"))(years_past_2C) 
reds_gradient_2C  <- colorRampPalette(c("#fddbc7", "#f3513a"))(years_future_2C)
grey_highlight_2C <- "#6a6a6a"

# 3. Stitch them together
final_palette_2C <- c(blues_gradient_2C, grey_highlight_2C, reds_gradient_2C)
p_right <- historical + 

  # MITIGATION LINES-----------------------------------------------------------------  
  
  geom_line(
    data = df_plot_2C |> filter(Type == "Mitigation"),
    aes(x = Year, 
        y = Emissions, 
        group = StartYear, 
        color = as.factor(StartYear), 
        size = (StartYear == focus_year)
        ), 
    alpha = 0.9
  ) +
  my_theme() +

  # COLORS ---------------------------------------------------------------------------

  scale_color_manual(values = final_palette_2C) + 
  
  # LINE WIDTHS ----------------------------------------------------------------------
  # Maps the "IsFocusYear" (TRUE/FALSE) to specific thicknesses
  scale_size_manual(
    values = c("FALSE" = 0.5, "TRUE" = 1), # False=Others, True=2019
    guide = "none" # This hides the legend for line thickness
  ) +
  
  # SCALES --------------------------------------------------------------------------


  scale_x_continuous(
    breaks = c(1980, 2000, 2020, 2040, 2060, 2080, 2100), 
    limits = c(1980, 2120), 
    expand = expansion(0)) + 
  scale_y_continuous(
    breaks = c(0,10,20,30,40), 
    labels = c("0", "10", "20", "30", ""), 
    expand = expansion(mult = c(0, 0.05))) 

p_right

Annotations

poly_df4 <- data.frame(
        #tip    wedt   topl    topr     botr   botl   wedb
  x = c( 2029,  2035,  2035, 2074.5,  2074.5, 2035,  2035), 
  y = c(42.33,    41, 42.55,  42.55,      33,   33,    39) 
)

poly_df5 <- data.frame(
        #tip      wedt      topl      topr      botr     botl      wedb
  x = c( 2031,    2039,      2039,   2081.5,  2081.5,    2039,    2039), 
  y = c( 32.3,      31,      32.3,     32.3,      24,      24,      30) 
)

poly_df6 <- data.frame(
        #tip    wedb    botr    topr     topl   botl   wedt              
  x = c( 2067,   2079,  2110,   2110,    2075,  2075,  2077), 
  y = c(10.13,     14,    14,   23.3,    23.3,    14,    14) 
)
p_right<-p_right+
  
  # TITTLE AND SUBTTILES----------------------------------------------------------------

  labs(
    title = "...but 2°C remains achievable",
    subtitle = "Each line shows the trajectory of annual emissions cuts required for\na greater than 50 per cent chance of staying below 2°C, depending\non the year that emissions peak"
  ) +
  
  # GIGATONS OF CO2--------------------------------------------------------------------

  annotate("text", x = 1980, y = 40, label = "40 Gt", hjust = 1.4, vjust = -0.5, size=7, color = "grey30") + 
  annotate("text", x = 1980, y = 40, label = "CO[2]", parse = TRUE, hjust = 1.5, vjust = 1, size=7, color = "grey30") + 
  
  # SOURCE-----------------------------------------------------------------------------

  annotate("text", x = 1980, y = 0, label = "Source: Robbie Andrews/Data:GCP/Emissions budget from IPCC AR6", hjust = 0.04, vjust = 6, color = "grey50", size=7.5) + 
  coord_cartesian(xlim = c(1980, 2100), ylim = c(-0.1, 44), clip = "off") +
  
  # POLIGONS-----------------------------------------------------------------------------
  
  # The Faint Grey Line that connects 2019 (graph peak) to 2026 (wedge tip)
  annotate("segment",x = 2019, xend = 2029,y = 42.33, yend = 42.33,color = "grey70",size = 0.5) +
  geom_polygon(data = poly_df4,aes(x = x, y = y),fill = "#0055AA",color = NA) +
  geom_polygon(data = poly_df5,aes(x = x, y = y),fill = "#0055AA",color = NA) +
  geom_polygon(data = poly_df6,aes(x = x, y = y),fill = "#0055AA",color = NA) +

  # WHITE TEXT 4 ------------------------------------------------------------------------

  annotate("text",x = 2037,y = 40.6,label = "Keeping emissions",hjust = 0,vjust = 0, color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2037,y = 38.4,label = "constant for 10 years",hjust = 0,vjust = 0, color = "white",size = 5.45,lineheight = 1.0) +
  annotate("text",x = 2037,y = 36.1,label = "would require subsequent",hjust = 0,vjust = 0, color = "white",size = 5.45,lineheight = 1.0)  +
  annotate("text",x = 2037,y = 33.8,label = "cuts of 7 per cent per year",hjust = 0,vjust = 0,color = "white",size = 5.45,lineheight = 1.0)+
  
  # WHITE TEXT 5 ------------------------------------------------------------------------

  annotate( "text", x = 2041, y = 30.3, label = "If we start cutting emissions", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 )+ 
  annotate( "text", x = 2041, y = 28.1, label = "today, we need to do so at a", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 ) + 
  annotate( "text", x = 2041, y = 25.9, label = "rate of 5 per cent per year", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 )+
  
  # WHITE TEXT 6 ------------------------------------------------------------------------

  annotate( "text", x = 2077, y = 21.3, label = "If we had started in", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 ) + 
  annotate( "text", x = 2077, y = 19.1, label = "2000, we would only", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 ) + 
  annotate( "text", x = 2077, y = 16.9, label = "have needed to cut by", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 ) + 
  annotate( "text", x = 2077, y = 14.7, label = "1 per cent each year", hjust = 0, vjust = 0, color = "white", size = 5.45, lineheight = 1.0 )

p_right

Combined

In order to replicate the graph we need to create a dividing line and then combine the two plots into one with the line in between them.

# Create a function to draw a vertical line as a grob
vertical_line_grob <- function() {
  linesGrob(
    x = unit(0.5, "npc"),
    y = unit(c(0.05, 0.95), "npc"),
    gp = gpar(col = "grey50", lwd = 1.5)
  )
}

# Create the combined layout with divider
combined_with_divider <- grid.arrange(
  p,
  vertical_line_grob(),
  p_right,
  ncol = 3,
  widths = c(1, 0.01, 1)  # Middle column is very thin for the line
)
combined_with_divider
TableGrob (1 x 3) "arrange": 3 grobs
  z     cells    name                  grob
1 1 (1-1,1-1) arrange        gtable[layout]
2 2 (1-1,2-2) arrange lines[GRID.lines.207]
3 3 (1-1,3-3) arrange        gtable[layout]

Improvement

The original graph provides redundant information. Historical data is the same for both plots. All the blue polygons are overwhelming and don’t necessarily give a sense of understanding the significance of the colored lines.

For this reason, we take away most of the mitigation lines since it’s muddles the story. However we do keep the purpose of showing the contrast in slopes between trying to shoot for a maximum of 1.5C warming vs 2C warming over pre-industrial levels.

p_improved <- historical + 
    
  # MITIGATION CURVES: 2°C (Blue)-------------------------------------------------------------

  geom_line(
    data = df_plot_2C |> filter(Type == "Mitigation", StartYear %in% c(2000, 2026)), 
    aes(x = Year, y = Emissions, group = StartYear, 
        size = (StartYear == 2019),
        color = "2C"), # Mapping for legend
    linetype = "dashed",    
    alpha = 0.9  
  ) +  
  
  # MITIGATION CURVES: 1.5°C (Red) ---------------------------------------------------------
  geom_line(
    data = df_plot |> filter(Type == "Mitigation", StartYear %in% c(2000, 2026)), 
    aes(x = Year, y = Emissions, group = StartYear, 
        size = (StartYear == 2019),
        color = "1.5C"), # Mapping for legend
    linetype = "dashed",    
    alpha = 0.9
  ) +
  
  my_theme() +

  # COLOR AND LEGEND ------------------------------------------------------
  scale_color_manual(
    # Break the title manually to control width
    name = "Trajectory of annual emissions cuts\nrequired to stick to the carbon budget*\nfor an even chance of staying below:",
    values = c("1.5C" = "#f3513a", "2C" = "#0055AA"),
    labels = c("1.5C" = "1.5°C", "2C" = "2°C")
  ) +
  
  # SIZE ------------------------------------------------------
  scale_size_manual(values = c("FALSE" = 0.5), guide = "none") +
  
  # SCALES ---------------------------------------------------------
  scale_x_continuous(breaks = c(1980, 2000, 2020, 2040, 2060, 2080, 2100), limits = c(1980, 2120), expand = expansion(0)) + 
  scale_y_continuous(breaks = c(0, 10, 20, 30, 40), labels = c("0", "10", "20", "30", ""), expand = expansion(mult = c(0, 0.05))) + 

  # LABELS ---------------------------------------------------------
  labs(
    title = "The path to net zero emissions is increasingly steep",
    subtitle = "Starting in 2000 would have meant a reduction of 4% a year for the 1.5°C goal and 2% for the 2°C goal.\nIf we wait till 2026 the slope will become 86% and 8% respectively.\nEven that second scenario is hard given that the pandemic's 2020 record drop was only 5.8%"
  ) +

  theme(
    # 1. Place legend on the right
    legend.position = "right",
    
    # 2. Align legend to the top-left of the margin area (anchors it near the subtitle)
    legend.justification = c(0, 1), 
    
    # 3. REDUCE the right margin. 
    plot.margin = margin(t = 0.9, r = 0.5, b = 1.5, l = 0.8, unit = "cm"),

    # 4. Legend Styling
    legend.title = element_text(size = 23, face = "italic", color = "grey30", lineheight = 0.8),
    legend.text = element_text(size = 21, face = "bold", margin = margin(t = 5, b = 5)), 
    legend.key = element_rect(fill = NA),
    legend.box.margin = margin(t = 60, l = 10) 
  ) +

  # GUIDE ---------------------------------------------------------
  # This stacks the title on top of the keys to save horizontal width

  guides(
    color = guide_legend(
      title.position = "top", 
      title.hjust = 0,
      ncol = 1
    )
  ) +

  # ANNOTATIONS ---------------------------------------------------------
  annotate("text", x = 1980, y = 40, label = "40 Gt", hjust = 1.4, vjust = -0.5, size=6, color = "grey30") + 
  annotate("text", x = 1980, y = 40, label = "CO[2]", parse = TRUE, hjust = 1.5, vjust = 1, size=6, color = "grey30") + 
    annotate("text", x = 1980, y = 0, label = "Source: Robbie Andrews/Data:GCP/Emissions budget from IPCC AR6", hjust = 0.04, vjust = 6, color = "grey50", size=7) +
  annotate("text", x = 1981, y = 0, label = "*Carbon budget estimated around 130 GtCO2 for a 50% chance of staying within 1.5°C and 525 GtCO2 for the 2°C goal", hjust = 0.04, vjust = 8, color = "grey50", size=7) + 
  
  coord_cartesian(xlim = c(1980, 2100), ylim = c(-0.1, 44), clip = "off")

p_improved

Thanks to this visualization we can see how impossible it is to stay within the budget for 1.5ºC of warming.

Footnotes

    Reuse

    Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

    Citation

    For attribution, please cite this work as

    De-Taranco-López (2026, Jan. 14). Data Visualization | MSc CSS: The path to 1.5 Celsius. Retrieved from https://csslab.uc3m.es/dataviz/projects/2025/100454765/

    BibTeX citation

    @misc{de-taranco-lópez2026the,
  author = {De-Taranco-López, Guillermo},
  title = {Data Visualization | MSc CSS: The path to 1.5 Celsius},
  url = {https://csslab.uc3m.es/dataviz/projects/2025/100454765/},
  year = {2026}
}