Was ist Wachstum - Decoupling Abbildung

decoupling.R

library(WDI)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(icaeDesign)
library(ggpubr)

if (FALSE) {
  wb_data <- WDI::WDI(
    country = c("DE", "CN", "IN", "ID"), start = 2000, end = 2022,
    indicator = c(
      "GHG" = "EN.GHG.ALL.MT.CE.AR5",
      "GDP" = "NY.GDP.MKTP.PP.KD",
      "GDPpc" = "NY.GDP.PCAP.PP.KD",
      "GDP_USD2015" = "NY.GDP.MKTP.KD"
    )
  )
  fwrite(wb_data, "wb_data.csv")
}

wb_data <- fread("wb_data.csv")
dmc_data <- fread("dmc_data.csv")

dmc_data_prep <- dmc_data |>
  rename("DMC_perGDP" = `12.2.2 - Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars) - EN_MAT_DOMCMPG - Total or no breakdown`) |>
  select(Code, Year, DMC_perGDP)
head(dmc_data_prep)

decoupling_data <- full_join(
  wb_data, dmc_data_prep,
  by = c("iso3c" = "Code", "year" = "Year")
) |>
  mutate(DMC = DMC_perGDP * GDP_USD2015) |>
  select(country, year, GHG, GDP, DMC)

# Plot 1: Keine Entkopplung

coupling_data <- decoupling_data |>
  select(-GHG) |>
  filter(country == "Indonesia") |>
  mutate(IMV = DMC / first(DMC), BIP = GDP / first(GDP)) |>
  pivot_longer(
    cols = c("IMV", "BIP"),
    names_to = "Variable",
    values_to = "Value"
  )

coupling_plot <- ggplot(
  data = coupling_data, mapping = aes(x = year, y = Value, color = Variable, linetype = Variable)
) +
  geom_line() +
  scale_y_continuous(labels = scales::label_percent()) +
  labs(
    title = "Dauerhafte Kopplung",
    subtitle = "Materialverbrauch und BIP in Indonesian",
    caption = "Daten: Weltbank, Our World in Data."
  ) +
  # scale_color_manual(values = c("BIP" = "#0072B2", "DMC" = "#D55E00")) +
  scale_color_grey() +
  scale_linetype_manual(values = c("BIP" = "twodash", "IMV" = "longdash")) +
  theme_icae() +
  theme(
    axis.title = element_blank(),
    plot.subtitle = element_text(
      colour = icaeDesign::get_euf_colors("grey"),
      hjust = 0.5, size = 12, margin = margin(t=5)
    )
  )
coupling_plot

# Plot 2: Relative Entkopplung

rel_decoupling_data <- decoupling_data |>
  select(-DMC) |>
  filter(country == "China") |>
  mutate(THG = GHG / first(GHG), BIP = GDP / first(GDP)) |>
  pivot_longer(
    cols = c("THG", "BIP"),
    names_to = "Variable",
    values_to = "Value"
  )

rel_decoupling_plot <- ggplot(
  data = rel_decoupling_data, 
  mapping = aes(x = year, y = Value, color = Variable, linetype = Variable)
) +
  geom_line() +
  scale_y_continuous(labels = scales::label_percent()) +
  # scale_color_manual(values = c("BIP" = "#0072B2", "GHG" = "#D55E00")) +
  scale_color_grey() +
  scale_linetype_manual(values = c("BIP" = "twodash", "THG" = "longdash")) +
  labs(
    title = "Relative Entkopplung",
    subtitle = "BIP und THG in China",
    caption = "Daten: Weltbank."
  ) +
  theme_icae() +
  theme(
    axis.title = element_blank(),
    plot.subtitle = element_text(
      colour = icaeDesign::get_euf_colors("grey"),
      hjust = 0.5, size = 12, margin = margin(t=5)
    )
  )
rel_decoupling_plot

# Plot 3: Absolute Entkopplung
abs_decoupling_data <- decoupling_data |>
  select(-DMC) |>
  filter(country == "Germany") |>
  mutate(THG = GHG / first(GHG), BIP = GDP / first(GDP)) |>
  pivot_longer(
    cols = c("THG", "BIP"),
    names_to = "Variable",
    values_to = "Value"
  )

abs_decoupling_plot <- ggplot(
  data = abs_decoupling_data, 
  mapping = aes(x = year, y = Value, color = Variable, linetype = Variable)
) +
  geom_line() +
  scale_y_continuous(labels = scales::label_percent()) +
  # scale_color_manual(values = c("BIP" = "#0072B2", "GHG" = "#D55E00")) +
  scale_color_grey() +
  scale_linetype_manual(values = c("BIP" = "twodash", "THG" = "longdash")) +
  labs(
    title = "Absolute Entkopplung",
    subtitle = "BIP und GHG in Deutschland",
    caption = "Daten: Weltbank."
  ) +
  theme_icae() +
  theme(
    axis.title = element_blank(),
    plot.subtitle = element_text(
      colour = icaeDesign::get_euf_colors("grey"),
      hjust = 0.5, size = 12, margin = margin(t=5)
    )
  )
abs_decoupling_plot

# Finaler plot

full_plot <- ggpubr::ggarrange(
  coupling_plot, rel_decoupling_plot, abs_decoupling_plot,
  ncol = 3
)
full_plot <- annotate_figure(
  full_plot,
  bottom = text_grob(
    "Abkürzungen: IMV - Inländischer Materialverbrauch; THG - Treibhausgase.",
    hjust = 1,       # Right-align; use 0 for left, 0.5 for center
    x = 1,           # Position at far right
    face = "italic",
    size = 12
  )
)

ggsave(
  plot = full_plot, 
  filename = "Kapitel_04_Grafik_01.pdf", 
  width = 9, 
  height = 4)

DMC-DataSource.md

Domestic material consumption per gross domestic product - Data package

This data package contains the data that powers the chart "Domestic material consumption per gross domestic product" on the Our World in Data website. It was downloaded on April 30, 2025.

Active Filters

A filtered subset of the full data was downloaded. The following filters were applied:

CSV Structure

The high level structure of the CSV file is that each row is an observation for an entity (usually a country or region) and a timepoint (usually a year).

The first two columns in the CSV file are "Entity" and "Code". "Entity" is the name of the entity (e.g. "United States"). "Code" is the OWID internal entity code that we use if the entity is a country or region. For normal countries, this is the same as the iso alpha-3 code of the entity (e.g. "USA") - for non-standard countries like historical countries these are custom codes.

The third column is either "Year" or "Day". If the data is annual, this is "Year" and contains only the year as an integer. If the column is "Day", the column contains a date string in the form "YYYY-MM-DD".

The final column is the data column, which is the time series that powers the chart. If the CSV data is downloaded using the "full data" option, then the column corresponds to the time series below. If the CSV data is downloaded using the "only selected data visible in the chart" option then the data column is transformed depending on the chart type and thus the association with the time series might not be as straightforward.

Metadata.json structure

The .metadata.json file contains metadata about the data package. The "charts" key contains information to recreate the chart, like the title, subtitle etc.. The "columns" key contains information about each of the columns in the csv, like the unit, timespan covered, citation for the data etc..

About the data

Our World in Data is almost never the original producer of the data - almost all of the data we use has been compiled by others. If you want to re-use data, it is your responsibility to ensure that you adhere to the sources' license and to credit them correctly. Please note that a single time series may have more than one source - e.g. when we stich together data from different time periods by different producers or when we calculate per capita metrics using population data from a second source.

Detailed information about the data

12.2.2 - Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars) - EN_MAT_DOMCMPG - Total or no breakdown

Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars) Last updated: August 27, 2024
Next update: August 2026
Date range: 2000–2022
Unit: kilograms per constant usd

How to cite this data

In-line citation

If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:
United Nations Environment Programme – processed by Our World in Data

Full citation

United Nations Environment Programme – processed by Our World in Data. “12.2.2 - Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars) - EN_MAT_DOMCMPG - Total or no breakdown” [dataset]. United Nations Environment Programme, “UN Statistics Division, Country Files from the UNSD/UNEP data collection on environment statistics (available at: https://unstats.un.org/unsd/envstats/country_files) - UNSD/UNEP Questionnaire, WESR / Global Material Flows Database, Eurostat” [original data]. Source: United Nations Environment Programme – processed by Our World In Data

How is this data described by its producer - United Nations Environment Programme?

Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars)

Further information available at: https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-02.pdf

Source

United Nations Environment Programme – UN Statistics Division, Country Files from the UNSD/UNEP data collection on environment statistics (available at: https://unstats.un.org/unsd/envstats/country_files) - UNSD/UNEP Questionnaire, WESR / Global Material Flows Database, Eurostat

Retrieved on: 2024-08-27
Retrieved from: https://unstats.un.org/sdgs/dataportal

dmc_data.csv

wb_data.csv