danielrosehill/ifvi_valuefactors_deriv

🚀 What if companies' environmental impacts could be quantified in monetary terms!?

🌍 About The Global Value Factors Explorer Dataset

The Global Value Factors Database, released by the International Foundation for Valuing Impacts during UN Climate Week NYC 2023, provides a set of almost 100,000 “value factors” for converting environmental impacts into monetary terms.

The GVFD covers 430 different environmental impacts across four main categories of impact: air pollution, land use and conversion, waste and water pollution . With the exception of the value factor for greenhouse gas emissions, for which a single value factor is provided ($236/tco2e), the value factors are geographically stratified (in other words, the value factors are both impact-specific and geolocation-specific). In total, there are 268 geolocations in the dataset reflecting all the world's recognised sovereigns as well as some international dependencies. In addition, one set of value factors, air pollution, provides data at the level of US states.

Key Data Parameters

Parameter	Value
Value Factors	Almost 100,000 "value factors" for converting quantitative environmental data into monetary equivalents (USD)
Geolocations	268 geolocations (world sovereigns plus US states - for air pollution methodology only)
Impacts Covered	Air pollution; GHG emissions; land use and conversion; water use and pollution; waste.
Parameter Source Data	Global Value Factors Database as released by the International Foundation for Valuing Impacts in September 2024
License	Licensing in accordance with IFVI, license link

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Download Statistics

Impact Accounting

The value factors are intended for use by account preparers preparing financial statements which integrate their environmental and social impacts alongside their traditional financial impacts, unifying all their holistic impacts into one set of financial calculations While the GVFD covers only environmental factors, a key part of the IFVI's mission is also developing methodologies for quantifying social impacts.

In order to fulfill their intended purpose, the value factors need to be matched with the raw quantitative environmental data which each value factor is intended to convert into monetary terms (the value factors are expressed as conversions to the US dollar).

Additional Use-Cases

Note:

The following suggested additional use cases were authored by me and do not bear the formal endorsement of IFVI.

Rather, my intention in sharing them is to stimulate thought into how the iterative process of arriving at methods of converting environmental data into monetary terms could have uses beyond impact accounting. This list is extremely non-exhaustive and many more potential interesting uses for this data can be suggested.

Use Case	Description
Tax Credits	The value factors could provide a framework for governments to devise and implement incentives to encourage companies to a) implement robust strategies around the collection and measurement of environmental parameters, and b) encourage those doing so with reduced taxation, which could also be used to offset the cost of collection programs.
Comparing Financial Performance And Sustainability	There is vigorous interest from a wide variety of stakeholders in understanding the extent to which companies' environmental performance and profitability are correlated. This analysis is enabled by having a diverse range of environmental parameters that can be monetized. Given the significant variability in the environmental parameters that publicly traded companies collect and disclose, a broad array of “value factors” is particularly advantageous, as it increases the likelihood that a meaningful amount of data will be available for any given reporter. Impact accounting involves the direct integration of these value factors by account preparers; however, it is equally important for external entities, such as sector analysts and environmental lobby groups, to use these factors to create composites of financial and sustainability reporting by applying them to publicly released financial data. Publicly traded companies inherently release financial data, and an increasing number also consistently publish sustainability data in quantitative terms. Value factors serve as a bridge between these two datasets, enabling even approximations of the theorized financial effects of environmental impacts to be assessed and considered.
Policy Formulation	In our current economic system, companies are often recused from financially contributing to mitigate environmental impacts attributed to them. Given scarce public resources and fairness concerns, many argue companies should act as financial participants in these programs. Monetizing their environmental impacts could provide a “bill” for companies' financial effects, aiding in policy arguments and garnering support for corporate responsibility as a true obligation rather than voluntary action.

About This Data Project (Derivative Database)

This derivative dataset was prepared by me, Daniel Rosehill, in order to facilitate the exploration and analysis of this dataset by non-commercial users. I believe that there is a strong policy interest in the question of how companies' impacts can be properly accounted for, recognising their societal and planetary effects.

To facilitate such analysis, I undertook a data reformatting process converting the initial version of the IFVI data from its original format (XLSM) and providing it as extracted comma-separated value files, as well as JSON structured in various hierarchies, some reflecting a territorial hierarchy (i.e. by geolocation) and others reflecting an impact-first hierarchy (in other words, with the impacts as the primary level, and the geo-stratified value factors nested under them).

The CSV files should provide the flexibility for users to work with the data as they see fit, while the JSON files direct towards specific vantage points and use cases for the data.

Use of the value factors is governed in accordance with the licensing terms provided by the IFVI (which, at the time of writing, provide for free usage for individual account preparers and non-commercial users.) Those looking to read the full official licence should refer to the website of the IFVI at www.ifvi.org

📜 Licensing

This derivative dataset is subject to the same terms of use as the original database, available in license.md at the repository root. These licensing conditions are stipulated by the International Foundation for Valuing Impacts. At the time of writing, the licensing terms provide for wide use of the data on a complimentary basis (including by account preparers) with limited exclusions to that position for those looking to integrate the data into commercial data products for which licensing charges apply. Questions regarding licensing of the database and requests for clarification regarding allowable uses and any other queries regarding compliance with the terms of their license should be referred to the IFVI.

📅 Versioning

This repository reflects GVFD Version 1 (October 15th, 2024). It is not guaranteed to be the most recent version. Consult the IFVI website for the latest data and updates. While this repository aims to mirror the original GVFD, using this data for official purposes requires referencing the complete IFVI documentation, which is not included here.

🗂️ Data Formatting

The source data has been restructured for various analytical perspectives:

Data Category	Description
By Methodology	JSON arrays organized by methodology parameters.
By Methodology, By Country	Mirrors the source database structure (except Land Use and Conversion, which are split into two files).
By Territory	Organizes data geographically by continent, territory, and US state (US states appear in one methodology). JSON files aggregate data from various methodology tabs.

Additional resources:

• CSV format data.
• metadata/ folder containing non-data items (e.g., notes from the original database tabs).

🛠️ Data Modifications

No material data changes were made. Modifications are limited to formatting and restructuring for analysis. Two non-material changes (documented in the changelog) are:

• Removal of US dollar signs for easier database integration.
• Standardization of 12 country names to more common versions (e.g., "Bahamas, The" to "Bahamas") and mapping all territories to their ISO-3166 Alpha-2 codes for clarity.

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📝 Release Notes For V2

This release standardises versioning for an early iteration (V2) of the derivative database of the IFVI Global Value Factors Database (GVFD).

This package consists of JSON representations of the original XLSM database contained in the original IFVI data release.

JSON hierarchies reflecting different organisations of the source data

The data tables in this derivative dataset are organised into various hierarchies to support different data analytics and visualisation use-cases:

• by-methodology This folder is divided into subfolders tracking the various methodologies used by the IFVI. The files it contains are "custom" (original) hierarchies representing the data. Not all the methodologies have data tables in this folder.
• by-methodology-by-country This folder maps most closely onto the original format in which the data was released and divides the database firstly by methodology and then by country (and then with impacts, values, etc)
• by-territory This folder consists of individual JSON files for the various countries and territories (including US states) that were included in some or all of the methodology data releases. The datasets here are organised firstly into geographical continents and then by country (or territory; some of the territories are not widely recognised as independent sovereigns). US states - which were included in one methodology - have their own subfolder.

Data Modifications (Non-Substantive)

This dataset (and the repository containing it) is a non-official derivative of the International Foundation for Valuing Impact (IFVI) Global Value Factors Database (GVFD) V1. This derivative dataset is intended to support the programmatic use of the Database for research-related analysis and visualisation.

This dataset intends to reflect an accurate reformatting of the source data at the time of its compilation. This version of the derivative dataset is based upon the first version of the GVFD as published by the IFVI on October 15th 2024.

No material edits have been made to the source data.

The following edits were made solely to support the intended use-case:

Removal of currency symbols

To streamline intake of these JSON files into database systems, non-integer data (currency symbols) were scrubbed from the dataset. As is noted in the metadata, the IFVI Database is standardised on the US Dollar.

Editing of country and territory names

To assist with geovisualisation use-cases, all countries and territories were matched with their corresponding alpha-2 values as defined by ISO 3166,

In order to render the names of countries and territories in more easily recognisable formatting, the names of 18 countries and territories were lightly reformatted.

For example "Bahamas, The" was renamed "Bahamas" and "Egypt, Arab Rep." was renamed as simply "Egypt."

Separation Of Non-Data Entities

• metadata This folder provides individual JSONs which capture the notes that were appended on each tab of the source XLSM
• reference A static snapshot of the supporting documentation (methodologies and user manuals) released by the IFVI alongside the data release

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Data Parameters By Impact Category

Air Pollution: Data Description

Title	Details
Dataset Name	Air Pollution Methodology
Methodology Status	Interim
Location-sensitive?	Yes
Territories provided	197 countries, 51 US states/territories (including Washington, D.C.)
Example parameters	PM2.5, PM10, SOx, NOx, NH3, VOC
Units	Metric tons per year (per pollutant)
Sample datapoint	Air Pollution_PM2.5_Urban_Primary Health

GHG Emissions: Data Description

Title	Details
Dataset Name	GHG Methodology
Methodology Status	Interim
Location-sensitive?	No
Territories provided	N/A
Example parameters	Global warming potential, carbon dioxide equivalency
Units	$/tCO2e (USD per metric ton of CO2 equivalent)
Sample datapoint	236.0 $/tCO2e

Land Conversion: Data Description

Title	Details
Dataset Name	Land Conversion Methodology
Methodology Status	Interim
Location-sensitive?	Yes
Territories provided	197 countries
Example parameters	Wheat - conventional, Oilseeds - conventional, Cashmere - sustainable, Forestry, Paved
Units	Hectares (for land use categories)
Sample datapoint	Land Conversion_Wheat - conventional_Lost Ecosystem Services

Land Use: Data Description:

Title	Details
Dataset Name	Land Use Methodology
Methodology Status	Interim
Location-sensitive?	Yes
Territories provided	197 countries
Example parameters	Wheat - conventional, Oilseeds - conventional, Cashmere - sustainable, Forestry, Paved
Units	Hectares (ha)
Sample datapoint	Land Use_Wheat - conventional_Lost Ecosystem Services

Waste: Data Description

Title	Details
Dataset Name	Waste Methodology
Methodology Status	Interim
Location-sensitive?	Yes
Territories provided	197 countries
Example parameters	Hazardous, Non-Hazardous; disposal methods: Landfill, Incineration, Unspecified
Units	Kilograms (kg)
Sample datapoint	Waste_Hazardous_Landfill_Leachate

Water Consumption: Data Description:

Title	Details
Dataset Name	Water Consumption Methodology
Methodology Status	Interim
Location-sensitive?	No
Territories provided	197 countries
Example parameters	Malnutrition, Water-borne disease, Resource cost, Ecosystem services
Units	Cubic meters (m³)
Sample datapoint	Water Consumption_N/A for WC_N/A for WC_Malnutrition

Water Pollution: Data Description:

Title	Details
Dataset Name	Water Pollution Methodology
Methodology Status	Interim
Location-sensitive?	Yes
Territories provided	197 countries
Example parameters	Phosphorus, Nitrogen, Heavy Metals (e.g., Cadmium, Lead, Mercury), Pesticides, Pharmaceuticals (e.g., Antibiotics, NSAIDs)
Units	Kilograms (kg)
Sample datapoint	Water Pollution_Phosphorus_N/A for this Category_Eutrophication

Sample Data Values By Methodology (CSV)

🧪 Sample Data

Air Pollution

Country,Category,Location,Impact,Units,Reference,Value
Afghanistan,PM2.5,Urban,Primary Health,/metric ton,Air Pollution_PM2.5_Urban_Primary Health,"40,495.28"
Afghanistan,PM2.5,Peri-Urban,Primary Health,/metric ton,Air Pollution_PM2.5_Peri-Urban_Primary Health,"34,468.58"
Afghanistan,PM2.5,Rural,Primary Health,/metric ton,Air Pollution_PM2.5_Rural_Primary Health,"19,386.52"
Afghanistan,PM2.5,Transport,Primary Health,/metric ton,Air Pollution_PM2.5_Transport_Primary Health,"31,346.36"
Afghanistan,PM2.5,N/A for PM2.5,Visibility,/metric ton,Air Pollution_PM2.5_N/A for PM2.5_Visibility,4.78
Afghanistan,SOx,Urban,Primary Health,/metric ton,Air Pollution_SOx_Urban_Primary Health,"13,398.15"
Afghanistan,SOx,Peri-Urban,Primary Health,/metric ton,Air Pollution_SOx_Peri-Urban_Primary Health,"13,345.45"
Afghanistan,SOx,Rural,Primary Health,/metric ton,Air Pollution_SOx_Rural_Primary Health,"6,694.38"
Afghanistan,SOx,Transport,Primary Health,/metric ton,Air Pollution_SOx_Transport_Primary Health,"10,893.71"
Afghanistan,SOx,N/A for SOx,Visibility,/metric ton,Air Pollution_SOx_N/A for SOx_Visibility,31.86
Afghanistan,NH3,Urban,Primary Health,/metric ton,Air Pollution_NH3_Urban_Primary Health,"12,148.59"
Afghanistan,NH3,Peri-Urban,Primary Health,/metric ton,Air Pollution_NH3_Peri-Urban_Primary Health,"10,340.57"
Afghanistan,NH3,Rural,Primary Health,/metric ton,Air Pollution_NH3_Rural_Primary Health,"5,815.95"
Afghanistan,NH3,Transport,Primary Health,/metric ton,Air Pollution_NH3_Transport_Primary Health,"9,403.91"
Afghanistan,NH3,N/A for NH3,Visibility,/metric ton,Air Pollution_NH3_N/A for NH3_Visibility,6.06
Afghanistan,PM10,Urban,Primary Health,/metric ton,Air Pollution_PM10_Urban_Primary Health,260.51
Afghanistan,PM10,Peri-Urban,Primary Health,/metric ton,Air Pollution_PM10_Peri-Urban_Primary Health,238.92
Afghanistan,PM10,Rural,Primary Health,/metric ton,Air Pollution_PM10_Rural_Primary Health,120.84

Land Conversion

Country,Category,Location,Impact,Units,Reference,Value
Afghanistan,Wheat - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Wheat - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
Afghanistan,"Vegetables, fruit, nuts - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Vegetables, fruit, nuts - conventional_N/A for LULC_Lost Ecosystem Services","14,424.09"
Afghanistan,"Cereals, grains - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Cereals, grains - conventional_N/A for LULC_Lost Ecosystem Services","12,573.76"
Afghanistan,Oilseeds - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Oilseeds - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
Afghanistan,"Sugarcane, sugarbeet - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Sugarcane, sugarbeet - conventional_N/A for LULC_Lost Ecosystem Services","12,573.76"
Afghanistan,Plant-based fibers - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Plant-based fibers - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
Afghanistan,Other crops - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Other crops - conventional_N/A for LULC_Lost Ecosystem Services,"12,573.76"
Afghanistan,Other crops - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Other crops - organic_N/A for LULC_Lost Ecosystem Services,"11,640.73"
Afghanistan,Other crops - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Other crops - sustainable_N/A for LULC_Lost Ecosystem Services,"10,870.67"
Afghanistan,"Bovine, sheep, goats, horses - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Bovine, sheep, goats, horses - conventional_N/A for LULC_Lost Ecosystem Services","14,200.25"
Afghanistan,"Bovine, sheep, goats, horses - organic",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Bovine, sheep, goats, horses - organic_N/A for LULC_Lost Ecosystem Services","13,676.30"
Afghanistan,"Bovine, sheep, goats, horses - sustainable",N/A for LULC,Lost Ecosystem Services,/ha,"Land Conversion_Bovine, sheep, goats, horses - sustainable_N/A for LULC_Lost Ecosystem Services","13,521.12"
Afghanistan,Cashmere - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Cashmere - conventional_N/A for LULC_Lost Ecosystem Services,"14,724.20"
Afghanistan,Cashmere - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Cashmere - organic_N/A for LULC_Lost Ecosystem Services,"13,676.30"
Afghanistan,Cashmere - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Cashmere - sustainable_N/A for LULC_Lost Ecosystem Services,"13,521.12"
Afghanistan,Forestry,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Forestry_N/A for LULC_Lost Ecosystem Services,"1,441.78"
Afghanistan,Paddy rice,N/A for LULC,Lost Ecosystem Services,/ha,Land Conversion_Paddy rice_N/A for LULC_Lost Ecosystem Services,"10,984.10"

Land Use

Country,Category,Location,Impact,Units,Reference,Value
Afghanistan,Wheat - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Wheat - conventional_N/A for LULC_Lost Ecosystem Services,216.64
Afghanistan,"Vegetables, fruit, nuts - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Vegetables, fruit, nuts - conventional_N/A for LULC_Lost Ecosystem Services",248.52
Afghanistan,"Cereals, grains - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Cereals, grains - conventional_N/A for LULC_Lost Ecosystem Services",216.64
Afghanistan,Oilseeds - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Oilseeds - conventional_N/A for LULC_Lost Ecosystem Services,216.64
Afghanistan,"Sugarcane, sugarbeet - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Sugarcane, sugarbeet - conventional_N/A for LULC_Lost Ecosystem Services",216.64
Afghanistan,Plant-based fibers - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Plant-based fibers - conventional_N/A for LULC_Lost Ecosystem Services,216.64
Afghanistan,Other crops - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Other crops - conventional_N/A for LULC_Lost Ecosystem Services,216.64
Afghanistan,Other crops - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Other crops - organic_N/A for LULC_Lost Ecosystem Services,200.56
Afghanistan,Other crops - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Other crops - sustainable_N/A for LULC_Lost Ecosystem Services,187.3
Afghanistan,"Bovine, sheep, goats, horses - conventional",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Bovine, sheep, goats, horses - conventional_N/A for LULC_Lost Ecosystem Services",244.66
Afghanistan,"Bovine, sheep, goats, horses - organic",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Bovine, sheep, goats, horses - organic_N/A for LULC_Lost Ecosystem Services",235.64
Afghanistan,"Bovine, sheep, goats, horses - sustainable",N/A for LULC,Lost Ecosystem Services,/ha,"Land Use_Bovine, sheep, goats, horses - sustainable_N/A for LULC_Lost Ecosystem Services",232.96
Afghanistan,Cashmere - conventional,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Cashmere - conventional_N/A for LULC_Lost Ecosystem Services,253.69
Afghanistan,Cashmere - organic,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Cashmere - organic_N/A for LULC_Lost Ecosystem Services,235.64
Afghanistan,Cashmere - sustainable,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Cashmere - sustainable_N/A for LULC_Lost Ecosystem Services,232.96
Afghanistan,Forestry,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Forestry_N/A for LULC_Lost Ecosystem Services,24.84
Afghanistan,Paddy rice,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Paddy rice_N/A for LULC_Lost Ecosystem Services,189.25
Afghanistan,Paved,N/A for LULC,Lost Ecosystem Services,/ha,Land Use_Paved_N/A for LULC_Lost Ecosystem Services,312.21

Waste

Country,Category,Location,Impact,Units,Reference,Value
Afghanistan,Hazardous,Landfill,Leachate,/kg,Waste_Hazardous_Landfill_Leachate,18.19
Afghanistan,Hazardous,Landfill,Waste GHGs,/kg,Waste_Hazardous_Landfill_Waste GHGs,179.15
Afghanistan,Hazardous,Landfill,Disamenity,/kg,Waste_Hazardous_Landfill_Disamenity,45.96
Afghanistan,Non-Hazardous,Landfill,Leachate,/kg,Waste_Non-Hazardous_Landfill_Leachate,0.3
Afghanistan,Non-Hazardous,Landfill,Waste GHGs,/kg,Waste_Non-Hazardous_Landfill_Waste GHGs,179.15
Afghanistan,Non-Hazardous,Landfill,Disamenity,/kg,Waste_Non-Hazardous_Landfill_Disamenity,45.96
Afghanistan,Hazardous,Incineration,Waste GHGs,/kg,Waste_Hazardous_Incineration_Waste GHGs,386.36
Afghanistan,Hazardous,Incineration,Disamenity,/kg,Waste_Hazardous_Incineration_Disamenity,3.01
Afghanistan,Hazardous,Incineration,Waste Air pollution,/kg,Waste_Hazardous_Incineration_Waste Air pollution,18.28
Afghanistan,Hazardous,Incineration,Heavy metals and dioxins,/kg,Waste_Hazardous_Incineration_Heavy metals and dioxins,4.93
Afghanistan,Non-Hazardous,Incineration,Waste GHGs,/kg,Waste_Non-Hazardous_Incineration_Waste GHGs,124.02
Afghanistan,Non-Hazardous,Incineration,Disamenity,/kg,Waste_Non-Hazardous_Incineration_Disamenity,3.01
Afghanistan,Non-Hazardous,Incineration,Waste Air pollution,/kg,Waste_Non-Hazardous_Incineration_Waste Air pollution,18.28
Afghanistan,Non-Hazardous,Incineration,Heavy metals and dioxins,/kg,Waste_Non-Hazardous_Incineration_Heavy metals and dioxins,4.93
Afghanistan,Hazardous,Unspecified,Leachate,/kg,Waste_Hazardous_Unspecified_Leachate,0.0
Afghanistan,Hazardous,Unspecified,Waste Air pollution,/kg,Waste_Hazardous_Unspecified_Waste Air pollution,18.28
Afghanistan,Hazardous,Unspecified,Heavy metals and dioxins,/kg,Waste_Hazardous_Unspecified_Heavy metals and dioxins,4.93
Afghanistan,Hazardous,Unspecified,Disamenity,/kg,Waste_Hazardous_Unspecified_Disamenity,3.01
Afghanistan,Hazardous,Unspecified,Waste GHGs,/kg,Waste_Hazardous_Unspecified_Waste GHGs,386.36
Afghanistan,Non-Hazardous,Unspecified,Leachate,/kg,Waste_Non-Hazardous_Unspecified_Leachate,0.3
Afghanistan,Non-Hazardous,Unspecified,Waste Air pollution,/kg,Waste_Non-Hazardous_Unspecified_Waste Air pollution,0.0
Afghanistan,Non-Hazardous,Unspecified,Heavy metals and dioxins,/kg,Waste_Non-Hazardous_Unspecified_Heavy metals and dioxins,0.0
Afghanistan,Non-Hazardous,Unspecified,Disamenity,/kg,Waste_Non-Hazardous_Unspecified_Disamenity,45.96
Afghanistan,Non-Hazardous,Unspecified,Waste GHGs,/kg,Waste_Non-Hazardous_Unspecified_Waste GHGs,179.15

Water Consumption

Country,Category,Location,Impact,Units,Reference,Value
Afghanistan,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.49
Afghanistan,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.06
Afghanistan,N/A for WC,N/A for WC,Resource cost,/m3,Water Consumption_N/A for WC_N/A for WC_Resource cost,0.32
Afghanistan,N/A for WC,N/A for WC,Ecosystem services,/m3,Water Consumption_N/A for WC_N/A for WC_Ecosystem services,0.28
Albania,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.02
Albania,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.13
Albania,N/A for WC,N/A for WC,Resource cost,/m3,Water Consumption_N/A for WC_N/A for WC_Resource cost,1.0
Albania,N/A for WC,N/A for WC,Ecosystem services,/m3,Water Consumption_N/A for WC_N/A for WC_Ecosystem services,1.94
Algeria,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.24
Algeria,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.0
Algeria,N/A for WC,N/A for WC,Resource cost,/m3,Water Consumption_N/A for WC_N/A for WC_Resource cost,0.43
Algeria,N/A for WC,N/A for WC,Ecosystem services,/m3,Water Consumption_N/A for WC_N/A for WC_Ecosystem services,0.08
American Samoa,N/A for WC,N/A for WC,Malnutrition,/m3,Water Consumption_N/A for WC_N/A for WC_Malnutrition,0.3
American Samoa,N/A for WC,N/A for WC,Water-borne disease,/m3,Water Consumption_N/A for WC_N/A for WC_Water-borne disease,0.11
American Samoa,N/A for WC,N/A for WC,

Water Pollution

Country,Category,Location,Impact,Units,Reference,Value
Afghanistan,Phosphorus,N/A for this Category,Eutrophication,/kg,Water Pollution_Phosphorus_N/A for this Category_Eutrophication,96.6218
Afghanistan,Nitrogen,N/A for this Category,Eutrophication,/kg,Water Pollution_Nitrogen_N/A for this Category_Eutrophication,0.0000
Afghanistan,Ag(I),Freshwater,Health,/kg,Water Pollution_Ag(I)_Freshwater_Health,41.6088
Afghanistan,Ag(I),Seawater,Health,/kg,Water Pollution_Ag(I)_Seawater_Health,0.8362
Afghanistan,Ag(I),Unspecified,Health,/kg,Water Pollution_Ag(I)_Unspecified_Health,41.6088
Afghanistan,As(III),Freshwater,Health,/kg,Water Pollution_As(III)_Freshwater_Health,"2,018.0068"
Afghanistan,As(III),Seawater,Health,/kg,Water Pollution_As(III)_Seawater_Health,169.1855
Afghanistan,As(III),Unspecified,Health,/kg,Water Pollution_As(III)_Unspecified_Health,"2,018.0068"
Afghanistan,As(V),Freshwater,Health,/kg,Water Pollution_As(V)_Freshwater_Health,"2,018.0068"
Afghanistan,As(V),Seawater,Health,/kg,Water Pollution_As(V)_Seawater_Health,169.1855
Afghanistan,As(V),Unspecified,Health,/kg,Water Pollution_As(V)_Unspecified_Health,"2,018.0068"
Afghanistan,Ba(II),Freshwater,Health,/kg,Water Pollution_Ba(II)_Freshwater_Health,64.0374
Afghanistan,Ba(II),Seawater,Health,/kg,Water Pollution_Ba(II)_Seawater_Health,12.9373

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Sample Data - JSON

Note: Afghanistan is the first country in the territories list ordered alphabetically so is chosen to demonstrate geographically-stratified examples

Air Pollution: PM 2.5 Values By Country

This JSON array - from V1 of the derivative dataset presents the value factors for particulate matter 2.5 (PM2.5).

Details of the air pollution dataset can be found here.

The value factors (value: in the array) are denominated in US dollars. The quantitative environmental parameters is metric tons of measured PM2.5 release.

This value factor is stratified by location.

{
    "PM2.5": {
        "Afghanistan": [
            {
                "Category": "PM2.5",
                "Location": "Urban",
                "Impact": "Primary Health",
                "Units": "/metric ton",
                "Reference": "Air Pollution_PM2.5_Urban_Primary Health",
                "Value": "40,495.28"
            },
            {
                "Category": "PM2.5",
                "Location": "Peri-Urban",
                "Impact": "Primary Health",
                "Units": "/metric ton",
                "Reference": "Air Pollution_PM2.5_Peri-Urban_Primary Health",
                "Value": "34,468.58"
            },
            {
                "Category": "PM2.5",
                "Location": "Rural",
                "Impact": "Primary Health",
                "Units": "/metric ton",
                "Reference": "Air Pollution_PM2.5_Rural_Primary Health",
                "Value": "19,386.52"
            },
            {
                "Category": "PM2.5",
                "Location": "Transport",
                "Impact": "Primary Health",
                "Units": "/metric ton",
                "Reference": "Air Pollution_PM2.5_Transport_Primary Health",
                "Value": "31,346.36"
            },
            {
                "Category": "PM2.5",
                "Location": "N/A for PM2.5",
                "Impact": "Visibility",
                "Units": "/metric ton",
                "Reference": "Air Pollution_PM2.5_N/A for PM2.5_Visibility",
                "Value": "4.78"
            }
        ]
    }
}

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Contributor Guidelines

Contributions to enhance this derivative dataset, making it more valuable, easier to navigate, and better suited for analytical and visualization use cases. If you have ideas or improvements, please consider contributing by following these steps:

• Submitting a Pull Request:
  Start by opening a pull request. A dedicated branch named Contributors Root is available as an initial entry point for contributions. If preferred, you can create individual contributor branches stemming from this root branch.

• Preserving the Original Structure:
  It is crucial to maintain the structure of the original derivative database as it mirrors the format published by the IFVI. Any modifications should not alter this original structure.

• Adding New Derivations:
  If you are adding new derivations or datasets, please organize them within the contributors subfolder located in the data root directory. This folder is a first-level directory designed to house all contributor additions while preserving the integrity of the original dataset.

Author (Source Database / GVFD)

• The International Foundation for Valuing Impacts (IFVI)

Author (Repository / Derivative Dataset)

• Daniel Rosehill

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