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---
language:
- en
pretty_name: IFVI Value Factors - Derivative Dataset For Analysis
---
[](https://github.com/danielrosehill/Global-Value-Factors-Explorer-Dataset)
[](https://huggingface.co/datasets/danielrosehill/ifvi_valuefactors_deriv)
[](https://ifvi.org/methodology/environmental-topic-methodology/interim-methodologies/#GlobalValueFactorDatabase)
## 🚀 What if companies' environmental impacts could be quantified in monetary terms!?
<a id="about-the-global-value-factors-explorer-dataset"></a>
## 🌍 About The Global Value Factors Explorer Dataset
The Global Value Factors Database, released by the [International Foundation for Valuing Impacts](https://www.ifvi.org) 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 (all recognized sovereigns plus some dependencies) |
| US States | Value factors for air pollution at the US state level |
| Impact Categories | Air pollution, land use and conversion, waste, water pollution |
| Methodologies | Interim methodologies from IFVI |
## 📂 Repository Structure
This repository is organized to facilitate use by policy makers, governmental actors, and other stakeholders:
```
ifvi_valuefactors_deriv/
├── core-data/ # Primary data files - the essential content
│ ├── by-policy-domain/ # Data organized by policy domains
│ ├── by-region/ # Data organized by geographic regions
│ ├── by-impact-type/ # Data organized by environmental impact type
│ └── aggregated/ # Consolidated datasets in multiple formats
│
├── documentation/ # All documentation related to the dataset
│ ├── data-dictionary/ # Explanations of data fields and values
│ ├── methodology/ # Documentation on IFVI methodologies
│ ├── policy-briefs/ # Policy-oriented summaries and use cases
│ └── technical-guides/ # Technical implementation guides
│
├── tools/ # Tools and utilities for working with the data
│ ├── conversion/ # Tools for data format conversion
│ ├── analysis/ # Analysis scripts and notebooks
│ └── visualization/ # Visualization tools and templates
│
├── examples/ # Example applications using the dataset
│
├── images/ # Images used in documentation
│ ├── cards/ # Card images for visual representation
│ └── graphics/ # Graphics and visual elements
│
└── resources/ # Additional resources and reference data
```
For more details on the repository structure, see [REPOSITORY_STRUCTURE.md](REPOSITORY_STRUCTURE.md).
## 📅 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.
## 📜 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.
<a id="data-formatting"></a>
## 🗂️ 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).
<a id="data-modifications"></a>
## 🛠️ 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.
## 🔧 Development and Maintenance
This repository includes scripts for maintaining download statistics and other metadata. To contribute or run these scripts locally:
1. Install the required dependencies:
```bash
pip install -r requirements.txt
```
2. Update download statistics manually:
```bash
python3 .github/scripts/update_stats.py
```
The repository is configured with GitHub Actions to automatically update download statistics daily.
<a id="release-notes-for-v2"></a>
---
# 📝 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)](https://ifvi.org/methodology/environmental-topic-methodology/interim-methodologies/).
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
---
# 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_N/A for LULC_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_N/A for LULC_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)
<a id="sample-data"></a>
## 🧪 Sample Data
### Air Pollution
```csv
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
```
---
## Sample Data - JSON
*Note: Afghanistan is the first country in the [territories list](/geo/territories) 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](https://github.com/danielrosehill/Global-Value-Factors-Explorer/tree/main/Data/GVFD-Deriv/data) presents the value factors for particulate matter 2.5 (PM2.5).
Details of the air pollution dataset can be found [here](specs/airpollution).
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.
```json
{
"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"
}
]
}
}
```
---
## 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)
[](https://www.ifvi.org)
## Author (Repository / Derivative Dataset)
- Daniel Rosehill
[](https://danielrosehill.com)
## Download Statistics
|