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                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Emissions per 1000 kilocalories",
                    "slug": "ghg_1000kcal",
                    "type": "Numeric",
                    "unit": "kilograms CO\u2082e",
                    "shortUnit": "kg",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Emissions per 100 grams of protein",
                    "slug": "ghg_100gprotein",
                    "type": "Numeric",
                    "unit": "kilograms CO\u2082e",
                    "shortUnit": "kg",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Emissions per 100 grams of fat",
                    "slug": "ghg_100gfat",
                    "type": "Numeric",
                    "unit": "kilograms CO\u2082e",
                    "shortUnit": "kg",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Land use per kilogram",
                    "slug": "land_use_kg",
                    "type": "Numeric",
                    "unit": "m\u00b2",
                    "shortUnit": "m\u00b2",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Land use per 1000 kilocalories",
                    "slug": "land_use_1000kcal",
                    "type": "Numeric",
                    "unit": "m\u00b2",
                    "shortUnit": "m\u00b2",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Land use per 100 grams of protein",
                    "slug": "land_use_100gprotein",
                    "type": "Numeric",
                    "unit": "m\u00b2",
                    "shortUnit": "m\u00b2",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Land use per 100 grams of fat",
                    "slug": "land_use_100gfat",
                    "type": "Numeric",
                    "unit": "m\u00b2",
                    "shortUnit": "m\u00b2",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Eutrophication per kilogram",
                    "slug": "eutrophication_kg",
                    "type": "Numeric",
                    "unit": "gPO\u2084",
                    "shortUnit": "g",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Eutrophication per 1000 kilocalories",
                    "slug": "eutrophication_1000kcal",
                    "type": "Numeric",
                    "unit": "gPO\u2084",
                    "shortUnit": "g",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Eutrophication per 100 grams of protein",
                    "slug": "eutrophication_100gprotein",
                    "type": "Numeric",
                    "unit": "gPO\u2084",
                    "shortUnit": "g",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Eutrophication per 100 grams of fat",
                    "slug": "eutrophication_100gfat",
                    "type": "Numeric",
                    "unit": "gPO\u2084",
                    "shortUnit": "g",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water scarcity per kilogram",
                    "slug": "water_scarcity_kg",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water scarcity per 1000 kilocalories",
                    "slug": "water_scarcity_1000kcal",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water scarcity per 100 grams of protein",
                    "slug": "water_scarcity_100gprotein",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water scarcity per 100 grams of fat",
                    "slug": "water_scarcity_100gfat",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water withdrawals per kilogram",
                    "slug": "water_kg",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water withdrawals per 1000 kilocalories",
                    "slug": "water_1000kcal",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water withdrawals per 100 grams of protein",
                    "slug": "water_100gprotein",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water withdrawals per 100 grams of fat",
                    "slug": "water_100gfat",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water withdrawals per 100 grams of protein",
                    "slug": "water_100gprotein",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                },
                {
                    "name": "Water withdrawals per 100 grams of fat",
                    "slug": "water_100gfat",
                    "type": "Numeric",
                    "unit": "liters",
                    "shortUnit": "L",
                    "sourceLink": "https://www.pnas.org/doi/full/10.1073/pnas.2120584119",
                    "sourceName": "Michael Clark et al (2022). Estimating the environmental impacts of 57,000 food products. PNAS.",
                    "additionalInfo": "This dataset quantifies the environmental impacts of 57,000 food products across retailers in the UK. It expands on previous analyses which quantify the environmental impacts of individual commodities, to quantify the impacts of products with multiple ingredients.\\n\\nTo do this, it uses the environmental footprints of commodities from the largest meta-analysis to date (Poore and Nemecek, 2018) and ingredient lists for specific products to calculate the total footprint of each product. For example, calculating the impact of \u2018Spaghetti Bolognese\u2019 would combine the impacts of the ingredients \u2013 pasta, minced beef, sauce, and any additional ingredients \u2013 in their weighted ratios.\\n\\nThe raw, underlying dataset has footprint data for 57,000 products. Our World in Data has combined these products into higher-level categories. For example, \u2018Steak pie\u2019 or \u2018Pizza\u2019 or \u2018Soy milk\u2019. To do this, we have calculated the average across the relevant products that fall into this category."
                }
            ]
        }
    ],
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    "subNavId": "explorers",
    "hasMapTab": "false",
    "selection": [
        "Almonds",
        "Bacon",
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