Was ist Wachstum - Abbildung zur großen Beschleunigung

acceleration_data.csv

Acceleration_Plots.R

here::i_am("Acceleration_Plots.R")
library(here)
library(readxl)
library(readr)
library(tidyr)
library(dplyr)
library(scales)
library(ggplot2)
library(ggrepel)
library(icaeDesign)

# Data preparation----------------------
## Read environmental indicators=============
# Data source: 
# Supplementary material to: https://doi.org/10.1177/2053019614564785
# For full replicability, please get the original data from this source
# Otherwise you can use the cleaned csv data acceleration_data.csv
use_original_data <- TRUE
if (use_original_data){
  co2_raw <- readxl::read_xlsx(
    path = here(
      "Acceleration_Data/earth_system_trends_1-carbon_dioxide2-methane3-nitrousoxide.xlsx"), 
    range = "E5:F2009", sheet = "Spline fits") |> 
    rename(
      year=`Year AD`,
      CO2=`CO2 Spline (ppm)  20 year attenuation`) |> 
    dplyr::mutate(region="Global")
  
  ocean_raw <- readxl::read_xlsx(
    path = here(
      "Acceleration_Data/earth_system_trends_6-ocean_acidification.xlsx"), 
    range = "A6:F257", sheet = "Hplus_RCP8.5") |> 
    rename(
      year=Year, 
      Ozeanversäuerung = `Mean H+ (nmol/kg)`
    ) |> 
    select(year, Ozeanversäuerung) |> 
    dplyr::mutate(region="Global")
  
  biosphere_raw <-  readxl::read_xlsx(
    path = here(
      "Acceleration_Data/earth_system_trends_12-terr_biosphere_degradation.xlsx"), 
    range = "A12:J18", 
    col_names = c("year", rep("NA", 8), "Biosphärenzerstörung"),
    sheet = "165g_gob10_zoekdata") |> 
    select(-starts_with("NA")) |> 
    dplyr::mutate(region="Global")
  
  gdp_raw <-  readxl::read_xlsx(
    path = here(
      "Acceleration_Data/socio-economic_trends_2_real_gdp.xlsx"), 
    range = "C10:G271", 
    sheet = "Data for graphs") |>
    rename(year=Year) |> 
    tidyr::pivot_longer(cols = -year, names_to = "region", values_to = "BIP") |> 
    dplyr::mutate(region=case_when(region == "World" ~ "Global", .default = region))
  
  water_raw <-  readxl::read_xlsx(
    path = here(
      "Acceleration_Data/socio-economic_trends_8_water_use.xlsx"), 
    range = "A8:E269", 
    sheet = "Data for graphs") |>
    rename(year=Year) |> 
    tidyr::pivot_longer(
      cols = -year, names_to = "region", values_to = "Wasserverbrauch") |> 
    dplyr::mutate(
      region=case_when(region == "World" ~ "Global", .default = region))
  
  transportation_raw <-  readxl::read_xlsx(
    path = here("Acceleration_Data/socio-economic_trends_10_transportation.xlsx"), 
    range = "A10:E270", 
    sheet = "Data for graphs") |>
    rename(year=Year) |> 
    tidyr::pivot_longer(
      cols = -year, names_to = "region", values_to = "Transport") |> 
    dplyr::mutate(
      region=case_when(region == "World" ~ "Global", .default = region))
  
  ## Merge environmental data----------
  full_environmental_data <- gdp_raw |> 
    full_join(co2_raw, by = c("year", "region")) |> 
    full_join(ocean_raw, by = c("year", "region")) |> 
    full_join(biosphere_raw, by = c("year", "region")) |> 
    full_join(water_raw, by = c("year", "region")) |> 
    full_join(transportation_raw, by = c("year", "region")) |> 
    rename(Jahr=year, Region=region)
  
  ## Read Publication data=====================
  year_range <- 1930:2010
  
  alle_artikel <- readxl::read_xlsx(
    path = here("Acceleration_Data/Chart_1_DFR_data.xlsx"), 
    range = "B4:D84", sheet = "color", 
    col_names = c("Artikel zu Wachstum", "Artikel", "Anteil")) |> 
    dplyr::mutate(
      Jahr=year_range, Disziplin ="Alle") |> 
    rename(
      `Wiss. Artikel zu Wachstum` = Anteil# Anteil von Gesamtpublikationen
    ) |> 
    select(c("Wiss. Artikel zu Wachstum", Jahr))
  
  vwl_artikel <- readxl::read_xlsx(
    path = here("Acceleration_Data/Chart_1_DFR_data.xlsx"), 
    range = "F4:H84", sheet = "color", 
    col_names = c("Artikel zu Wachstum", "Artikel", "Anteil")) |> 
    dplyr::mutate(
      Jahr=year_range, Disziplin ="Ökonomik")|> 
    rename(
      `Wirtschaftswiss. Artikel zu Wachstum` = Anteil# Anteil von Gesamtpublikationen
    ) |> 
    select(c("Wirtschaftswiss. Artikel zu Wachstum", Jahr))
  
  # powi_artikel <- readxl::read_xlsx(
  #   path = here("Acceleration_Data/Chart_1_DFR_data.xlsx"), 
  #   range = "J4:L84", sheet = "color", 
  #   col_names = c("Artikel zu Wachstum", "Artikel", "Anteil")) |> 
  #   dplyr::mutate(
  #     Jahr=year_range, Disziplin ="Politikwiss.")
  # 
  # soz_artikel <- readxl::read_xlsx(
  #   path = here("Acceleration_Data/Chart_1_DFR_data.xlsx"), 
  #   range = "N4:P84", sheet = "color", 
  #   col_names = c("Artikel zu Wachstum", "Artikel", "Anteil")) |> 
  #   dplyr::mutate(
  #     Jahr=year_range, Disziplin ="Soziologie")
  # 
  # hist_artikel <- readxl::read_xlsx(
  #   path = here("Acceleration_Data/Chart_1_DFR_data.xlsx"), 
  #   range = "R4:T84", sheet = "color", 
  #   col_names = c("Artikel zu Wachstum", "Artikel", "Anteil")) |> 
  #   dplyr::mutate(
  #     Jahr=year_range, Disziplin ="Geschichte")
  
  ngram_result <- read_csv(file = here("Acceleration_Data/ngram_growth.csv")) |> 
    dplyr::mutate(Disziplin ="Öffentliche Debatte") |> 
    mutate(Anteil=1000*Anteil) |> 
    rename(`Wachstum in öffentlicher Debatte`=Anteil) |> 
    select(-Disziplin)
  
  # Merge all data
  publikationsdaten <- full_join(
    x = alle_artikel, vwl_artikel, by = c("Jahr"))
  publikationsdaten <- full_join(
    x = publikationsdaten, ngram_result, by = c("Jahr"))
  
  ## Merge environmental and publication data--------------
  full_data <- full_join(
    x = filter(full_environmental_data, Region == "Global"), 
    y = publikationsdaten, 
    by = "Jahr")
  
  write_csv(full_data, file = here("Acceleration_Data/acceleration_data.csv"))
} else{
  full_data <- read_csv(here("Acceleration_Data/acceleration_data.csv"))
}


## Add exponential projections-------

# Function to create exponential projection based on data until 1950
create_exp_projection <- function(data) {
  # Filter data up to 1950
  early_data <- data %>% filter(Jahr <= 1950, !is.na(Wert))
  
  if(nrow(early_data) < 3) return(
    data.frame(Jahr = integer(), exp_proj = numeric()))
  
  # Exponential model (log-transform the response)
  # Add small constant to avoid log(0) issues
  early_data_exp <- early_data %>% 
    mutate(Wert_pos = pmax(Wert, 0.001))  # Ensure positive values
  
  exp_model <- tryCatch({
    lm(log(Wert_pos) ~ Jahr, data = early_data_exp)
  }, error = function(e) NULL)
  
  # Project from 1950 to 2010
  projection_years <- seq(1950, 2010, by = 1)
  
  # Exponential projection
  if(!is.null(exp_model)) {
    log_exp_proj <- predict(
      exp_model, newdata = data.frame(Jahr = projection_years))
    exp_proj <- exp(log_exp_proj)
  } else {
    exp_proj <- rep(NA, length(projection_years))
  }
  
  return(data.frame(Jahr = projection_years, exp_proj = exp_proj))
}


dimension_labels <- c(
  "Biosphärenzerstörung" = "Biosphärenzerstörung\n(% Artenhäufigkeits-Rückgang)",
  "BIP" = "BIP\n(Billionen USD)",
  "CO2" = "CO2-Konzentration\n(ppm)",
  "Ozeanversäuerung" = "Ozeanversäuerung\n(Wasserstoffionen)",
  "Transport" = "Motorisierung\n(Mio. neue Fahrzeuge/Jahr)",
  "Wasserverbrauch" = "Wasserverbrauch\n(km³ pro Jahr)",
  "Wiss. Artikel zu Wachstum" = "Wiss. Artikel zu Wachstum\n (% aller wiss. Publikationen)",
  "Wirtschaftswiss. Artikel zu Wachstum" = "Wirtschaftswiss. Artikel zu Wachstum\n (% aller VWL-Publikationen)",
  "Wachstum in öffentlicher Debatte" = "Wachstum in öffentlicher Debatte\n (1000 % aller Veröffentlichungen)" 
)

# Prepare data
plot_data <- full_data |> 
  dplyr::filter(Jahr>=1750) |> 
  dplyr::select(-Region) |> 
  pivot_longer(cols = -Jahr, values_to = "Wert", names_to = "Dimension") |> 
  filter(!is.na(Wert), Jahr<=2010) |> 
  mutate(Dimension = recode(Dimension, !!!dimension_labels)) |> 
  mutate(Dimension = factor(
    Dimension, 
    levels = c(
      "BIP\n(Billionen USD)",                                                           
      "Biosphärenzerstörung\n(% Artenhäufigkeits-Rückgang)",   
      "CO2-Konzentration\n(ppm)",                                                       
      "Ozeanversäuerung\n(Wasserstoffionen)",                                                   
      "Wasserverbrauch\n(km³ pro Jahr)",
      "Motorisierung\n(Mio. neue Fahrzeuge/Jahr)",
      "Wiss. Artikel zu Wachstum\n (% aller wiss. Publikationen)",           
      "Wirtschaftswiss. Artikel zu Wachstum\n (% aller VWL-Publikationen)",
      "Wachstum in öffentlicher Debatte\n (1000 % aller Veröffentlichungen)"                
    )))

# Create exponential projections for each dimension
no_projection <- c(      
  "Wiss. Artikel zu Wachstum\n (% aller wiss. Publikationen)",           
  "Wirtschaftswiss. Artikel zu Wachstum\n (% aller VWL-Publikationen)",
  "Wachstum in öffentlicher Debatte\n (1000 % aller Veröffentlichungen)"
  )

exp_projections <- plot_data %>%
  group_by(Dimension) %>%
  do(create_exp_projection(.)) %>%
  ungroup() |> 
  mutate(
    exp_proj = case_when(Dimension %in% no_projection ~ NA, .default = exp_proj)
  ) |> 
  filter(!is.na(exp_proj))

## Create graph---------

full_plot <- plot_data |> 
  filter(!is.na(Wert)) |> 
  ggplot(mapping = aes(x=Jahr, y=Wert)) +

  # Original data lines (solid blue)
  geom_line(na.rm = TRUE, linewidth = 0.8, color = "grey20") + 
  # Add exponential projections (dashed red)
  geom_line(
    data = exp_projections, aes(x = Jahr, y = exp_proj), 
    linetype = "dashed", color = "grey50",
    alpha = 0.8, linewidth = 0.6) +
  facet_wrap(~Dimension, scales = "free_y", ncol = 3) +
  scale_x_continuous(breaks = seq(1750, 2000, 25)) +
  scale_y_continuous(labels = label_number(big.mark = ".", decimal.mark = ",")) +
  scale_color_grey() +
  labs(
    x = "Jahr",
    y = "Wert",
    title = "Entwicklung globaler Umweltindikatoren, BIP und Wachstumsdiskurs seit 1750",
    subtitle = paste0(
      "Die \"Große Beschleunigung\" ab 1950 ist deutlich erkennbar ",
      "(Gestrichelte Linie: Exponentialprojektion basierend auf Daten bis 1950)"),
    caption = paste0(
      "Daten: Steffen et al. (2015); siehe Replikationsseite. ",
      "\nDie gestrichelten Linien sind Prognosen, die auf Daten bis zum Jahr ",
      "1950 basieren und exponenzielles Wachstum annehmen.")
  ) +
  theme_bw(base_size = 11) +
  theme(
    strip.text = element_text(size = 10, face = "bold"),
    strip.background = element_rect(fill = "grey90", color = "white"),
    panel.grid.minor = element_blank(),
    axis.title = element_blank(),
    axis.text = element_text(size = 9),
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(size = 13, face = "bold"),
    plot.subtitle = element_text(size = 11, color = "grey40"),
    plot.caption = element_text(size = 8, color = "grey60", hjust = 1),
    panel.spacing = unit(0.8, "lines")
  )

full_plot

ggsave(
  plot = full_plot, 
  filename = here("Beschleunigungsgraph.pdf"), 
  width = 10, height = 6)

ngram_data.R

here::i_am("Acceleration_Data/ngram_data.R")
library(here)
library(tibble)
library(readr)

# Source: https://books.google.com/ngrams/graph?content=economic+growth&year_start=1750&year_end=2010&corpus=en&smoothing=0&case_insensitive=false
# Accessed: September 12, 2025

ngram_result <- tibble("Jahr" = 1750:2010,
                       "Anteil" = as.double(as.character(
                         c(
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                         )
                       )))
write_csv(ngram_result, here("Acceleration_Data/ngram_growth.csv"))