Does the Food Industry Drag Us Down in Protecting the Earth?

Food US

This blog explores Greenhouse Gas Emissions related to food production and activities, specifically the issue with excessive food packaging and refrigerant gases.

Linda Lu

The Grocery Scenario

You walk into your familiar grocery store. You have a few reusable cotton tote bags in your purse. You feel awesome because you are a responsible consumer, caring about the toll of single-use plastic bags on the planet.

But as you pick up a bag of tangerines, another bag of three bell peppers, and some pre-packaged bacon, shoving them into your reusable bags, you are still creating plastic wastes. We are still creating tons of single-use plastic and other wastes: from the thin bag that contains the citrus, plastic wrappings, aluminum containers from already-made meals, most of which don’t get recycled and end up in landfills. You can read more on the problem of over-packaging here.

As some of us may be already conscious of “reduce, reuse, recycle,” the fundamental action verbs for living a sustainable life, one has to acknowledge that excessive use of plastic and other related packaging is a serious, industry problem. The global food and retail industry still heavily relies on plastic, polystyrene(Styrofoam), and aluminum when it comes to cheap wrapping options, despite knowing that they are extremely costly to the environment.

If you are wondering, there are multiple ways to measure the environmental cost of food packaging. This blog will take the approach that measures the amount of packaging utilized in the producing and retail industry, in terms of Greenhouse gas emissions generated by these activities.

This blog draws data from the EDGAR-FOOD dataset by Monica Crippa and her colleagues. It provides estimations of “greenhouse gas emissions (GHG: CO2, CH4, N2O, and fluoridated gases) from the [entire] food system at the global level over the time period 1990-2015.” EDGAR is an independent institute specializing in emission estimates, compared to what was reported by the European Member States or by Parties under the United Nations Climate Convention.

The EDGAR-FOOD dataset is critical to analyze the impact of excessive food packaging because the data incorporates GHG emissions from the very beginning of the production to the downstream of consumption, including processing, transport, packaging, and retail. It is thus significant for both policymakers and normal consumers to understand which life cycle of the food that we eat, or which sector of the food system, significantly contributes to GHG emission, exacerbating climate change and other environmental issues. The food-system-related emission will be at the focal point of this blog.

Data Preparation

All of the graphics included in this blog are enabled by the Highcharter package. It’s exactly like the Highcharts in the JavaScript realm, and it requires a license for commercial and governmental work. Students can get a non-commercial license by sign-up and then enjoy making awesome visualizations with ggplot2-like syntax and built-in interactive functions.

After loading the needed packages, I dived into data cleaning. The major things I have done were to reshape the datasets from a wide to long format and to clean up column names and values. You can click show code to view my process.

Show code
## Load needed datasets
GHG_sector <- read_csv(here
GHG_shares <- read_csv(here

## Initial data wrangling on GHG_sector
GHG_sector_long <- GHG_sector %>%
  mutate(across(Y_1990:Y_2006,as.numeric)) %>%
  pivot_longer(Y_1990:Y_2015, names_to = "Year", 
                              values_to = "Emission") %>%
  select(2, 5:8)
GHG_sector_long <- GHG_sector_long %>%
  mutate(Year = str_replace(
                GHG_sector_long$Year,"Y_","")) %>%
  mutate(Substance = str_replace(

## Initial data wrangling on GHG_shares
GHG_shares_sub <- GHG_shares %>%
  filter(country %in% c("USA", "BRA",
                        "CHN", "DEU", "IND",
name <- c("Brazil", "China", "Germany", "India",
          "South Korea", "United States")
GHG_shares_sub$name <- name
GHG_shares_sub <- GHG_shares_sub %>%
                  select(-1) %>%

Overview of food-system emission in major countries

Show code
## Make a paged table
GHG_shares_sub %>%
  paged_table(options = list(rows.print = 6, cols.print = 9))

This table depicts how the share of food-system-related greenhouse gas(GHG) emission to the total GHG emission changes over twenty-five years. Each row represents a country; I chose these six countries because some of them are the most developed economies, or the most populated countries, or have seen the most growth in recent years. The numeric value under different columns of year estimates in this specific country, how much percentage of GHG emission can be attributed to food-system related activities.

Focusing on the individual country, or each row, developed economies such as Germany and the United States show largely stable contribution of food-system related emission to their countries’ total, but there is also a small sign of an uptick in recent years. On the other hand, developing countries in the rest of the table exhibit a more drastic decrease in their shares. India, especially, has seen a 26 point drop, from its food-related GHG emission taking up more than half of the economy’s total emission (59%) in 1990 to only a third (33%) more recently.

What may explain the different trends is that 1)developed economies throughout the past thirty years have seen stable and slow population growth and formed a service and tech-oriented economy, 2)whereas developing countries have been turning away from agricultural-based economies to those specialized in manufacturing. Therefore, the former’s food-related shares in total emission are stable and comparatively lower, and the latter’s sees a decrease and remains on the high end because it takes more time to transform into a new kind of economy.

One should be cautious though, avoiding such a drawing conclusion that if a country’s food-system-related emission decreases, it’s going greener. This is not necessarily true as it’s only the percentage of emission related to the food industry and activities that experiences a fall, not the absolute value.

If you are interested in the absolute value, or global, nominal statistics, the EGDAR-FOOD dataset is also helpful.

Breaking down 2015 US food emissions

Now, let’s think more specifically, looking at the food-system-related GHG emissions in the United States in 2015.

Show code
GHG_sector_US_15 <- GHG_sector_long %>%
   filter(Name == "United States",
          Year == "2015")

tree_15 <- data_to_hierarchical(GHG_sector_US_15, 
           c(FOOD_system_stage, Substance), Emission)

lvl_opts <-  list(
    level = 1,
    dataLabels = list(
      enabled = TRUE,
      align = "left",
      verticalAlign = "top",
      style = list(fontSize = "18px", 
                   textOutline = FALSE, color = "white")
    level = 2,
    colorVariation = list(key = "brightness", to = 0.250),
    dataLabels = list(enabled = FALSE),
    style = list(fontSize = "16px", 
                 textOutline = FALSE, color = "white")

hchart(tree_15, type = "treemap",
       levelIsConstant = FALSE,
       allowDrillToNode = TRUE,
       levels = lvl_opts) %>%
        text = "2015 US Greenhouse Gas Emission from Food 
        only, Broken Down by Sectors")%>%
    hc_subtitle(text = "Food-system emissions explicated 
                in the context of the life cycle of food.") %>%
    hc_caption(text = "Emission are all measured in 
               kilotons(thousands of metric tons) of CO2 equivalent. 
               According to the EPA, one kiloton of CO2 can support 
               120 households' electricity use in a year. ") %>%
        enabled = TRUE, text = "Source: EDGAR",
        href = "") %>%
    hc_size(height = 500)

The treemap breaks down the full picture of emissions into smaller categories: production, transport, retail, packaging, etc. In all stages of production, there are energy use and cost, and most of them are less explicit and intuitive, which is why this treemap derived from the EDGAR dataset can be helpful.

You can further interact with the graphics by clicking on each rectangle, which will illustrate which greenhouse gas is involved in this stage of production, their respective, nominal values, and their percentage shares, illustrated by the size.

Returning to the issue of food packaging, which is a sizable input to the total food-related GHG emission, we can see that packaging by its own causes, shockingly, more emissions than our daily food cooking, heating, and other activities related to consumption. In other words, in an attempt to add up the emissions caused by all of the plastic packaging that you disposed of and did not think twice, you may actually derive a higher number than the energy unit written on your electricity bill. This drives home to the excessive use of packaging in the retail and food industry.

What’s more, the EPA provides a great equivalencies calculator for the public to make sense of what GHG emissions mean in familiar, everyday terms. If you wonder what 58,319.7 kilotons of GHG represent, go check it out yourself! (Hint: frenetic).

F-gases consumption in Retail industry from 1990 to 2015

Another area of concern raised by the treemap above is the amount of F-gases used and contributed to the US’s GHG emission from food, especially in the retail sector. This usually points to the HFC refrigerant used in air conditioning units and refrigeration equipment. It’s widely used in food factories and grocery stores to create a cooling effect, also in malls and restaurants when the weather is hot.

Show code
US_gas <- GHG_sector_long %>%
  filter(Name == "United States",
          Substance == "F-gases")
hchart(US_gas, "line", hcaes(x = Year, y = Emission)) %>%
    title = list(text = "Emissions in kilotons of C02 eq.")
  ) %>%
        text = "Trend shows F-gases Grow as Retail Booms")

The trend shows that between 1990 and 2015, as the US economy grows stronger, fueled by consumerism and higher spending power, the F-gases skyrocketed as well. This line graph provides strong supports to the current HFC and other F-gases phase-out plan , that we need a strong policy to mitigate F-gases emissions to cut down total GHG emissions.

Last words: Actions You Can Take to Make it Greener

The powerful EGDAR-FOOD data has allowed me to confirm and illustrate the problem of excessive food packaging and its toll on the environment. By dividing the US’s food-system-related GHG emission of 2015 into the different stages of food production, we can see that food packaging is a sizable contributor to the overall emission level.

Although the US’s emission from food takes up roughly one-fifth of its total yearly emission, based on the finding and experiences, the food and retail industry can certainly do better and contribute more towards the emission cut, advancing the mission of sustainability further.

We as consumers can demand our local grocery stores to only use wrappings when necessary, to avoid packaging multiple items as a set in advance, or to speed up the switch to biodegradable alternatives when one needs packaging. What’s more, to effectively address the F-gases Phase Out initiative, someone who’s environmentally conscious can stay attentive to the data, monitoring the effect and the speed of the phase-out.



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 ...".