The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
country_data: struct<Africa: list<item: struct<country: string, emoji: string, url: string>>, Asia: list<item: struct<country: string, emoji: string, url: string>>, Europe: list<item: struct<country: string, emoji: string, url: string>>, North America: list<item: struct<country: string, emoji: string, url: string>>, South America: list<item: struct<country: string, emoji: string, url: string>>, Oceania: list<item: struct<country: string, emoji: string, url: string>>>
vs
Territory: string
Topic: string
Category: string
Location: string
Impact: string
Reference: string
Units: string
Value: string
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 520, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
country_data: struct<Africa: list<item: struct<country: string, emoji: string, url: string>>, Asia: list<item: struct<country: string, emoji: string, url: string>>, Europe: list<item: struct<country: string, emoji: string, url: string>>, North America: list<item: struct<country: string, emoji: string, url: string>>, South America: list<item: struct<country: string, emoji: string, url: string>>, Oceania: list<item: struct<country: string, emoji: string, url: string>>>
vs
Territory: string
Topic: string
Category: string
Location: string
Impact: string
Reference: string
Units: string
Value: stringNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
IFVI Value Factors Dataset
Cumulative Download Statistics
📑 Navigation Index
- IFVI Value Factors Dataset
- 🔑 Key Parameters
📊 Dataset Overview
🌍 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.
📊 Data Exploration
To help you get started with exploring and visualizing the data, check out our Data Exploration Jupyter Notebook. This notebook provides a skeleton for loading, analyzing, and visualizing the IFVI Value Factors dataset.
🔑 Key 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.
📅 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.
🗂️ 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.
🔧 Development and Maintenance
This repository includes scripts for maintaining download statistics and other metadata. To contribute or run these scripts locally:
Install the required dependencies:
pip install -r requirements.txtUpdate download statistics manually:
python3 .github/scripts/update_stats.py
The repository is configured with GitHub Actions to automatically update download statistics daily.
📁 Data File Locations
Data Files Root
Aggregated Value Factors
Value Factors by Type/Methodology
Value Factors by Geography
CSV Format Data
🔄 Data By Impact Type
For detailed data organized by environmental impact type, visit the following directories:
🌫️ Air Pollution: Air Pollution Data
🏭 GHG Impacts (1 Impact Only): GHG Impacts Data
🌱 Land Conversion: Land Conversion Data
🌿 Land Use: Land Use Data
🗑️ Waste: Waste Data
💧 Water Consumption: Water Consumption Data
💦 Water Pollution (JSON Chunked Due To Size): Water Pollution Data
🌍 Country-Specific Data Navigation
🗺️ Data By Region
Continental Data Directories:
🌍 Africa
| Country | Data Link | Country | Data Link | Country | Data Link |
|---|---|---|---|---|---|
| 🇩🇿 Algeria | JSON | 🇦🇴 Angola | JSON | 🇧🇯 Benin | JSON |
| 🇧🇼 Botswana | JSON | 🇧🇫 Burkina Faso | JSON | 🇧🇮 Burundi | JSON |
| 🇨🇻 Cabo Verde | JSON | 🇨🇲 Cameroon | JSON | 🇨🇫 Central African Republic | JSON |
| 🇹🇩 Chad | JSON | 🇰🇲 Comoros | JSON | 🇨🇩 Congo, Dem. Rep. | JSON |
| 🇨🇬 Congo, Rep. | JSON | 🇨🇮 Cote d'Ivoire | JSON | 🇩🇯 Djibouti | JSON |
| 🇪🇬 Egypt | JSON | 🇬🇶 Equatorial Guinea | JSON | 🇪🇷 Eritrea | JSON |
| 🇸🇿 Eswatini | JSON | 🇪🇹 Ethiopia | JSON | 🇬🇦 Gabon | JSON |
| 🇬🇲 Gambia | JSON | 🇬🇭 Ghana | JSON | 🇬🇳 Guinea | JSON |
| Guinea-Bissau | JSON | 🇰🇪 Kenya | JSON | 🇱🇸 Lesotho | JSON |
| 🇱🇷 Liberia | JSON | 🇱🇾 Libya | JSON | 🇲🇬 Madagascar | JSON |
| 🇲🇼 Malawi | JSON | 🇲🇱 Mali | JSON | 🇲🇷 Mauritania | JSON |
| 🇲🇺 Mauritius | JSON | 🇲🇦 Morocco | JSON | 🇲🇿 Mozambique | JSON |
| 🇳🇦 Namibia | JSON | 🇳🇪 Niger | JSON | 🇳🇬 Nigeria | JSON |
| 🇷🇼 Rwanda | JSON | 🇸🇹 Sao Tome and Principe | JSON | 🇸🇳 Senegal | JSON |
| 🇸🇨 Seychelles | JSON | 🇸🇱 Sierra Leone | JSON | 🇸🇴 Somalia | JSON |
| 🇿🇦 South Africa | JSON | 🇸🇸 South Sudan | JSON | 🇸🇩 Sudan | JSON |
| 🇹🇿 Tanzania | JSON | 🇹🇬 Togo | JSON | 🇹🇳 Tunisia | JSON |
| 🇺🇬 Uganda | JSON | 🇿🇲 Zambia | JSON | 🇿🇼 Zimbabwe | JSON |
🌏 Asia
| Country | Data Link | Country | Data Link | Country | Data Link |
|---|---|---|---|---|---|
| 🇦🇫 Afghanistan | JSON | 🇦🇲 Armenia | JSON | 🇦🇿 Azerbaijan | JSON |
| 🇧🇭 Bahrain | JSON | 🇧🇩 Bangladesh | JSON | 🇧🇹 Bhutan | JSON |
| 🇧🇳 Brunei Darussalam | JSON | 🇰🇭 Cambodia | JSON | 🇨🇳 China | JSON |
| 🇨🇾 Cyprus | JSON | 🇬🇪 Georgia | JSON | 🇭🇰 Hong Kong SAR, China | JSON |
| 🇮🇳 India | JSON | 🇮🇩 Indonesia | JSON | 🇮🇷 Iran | JSON |
| 🇮🇶 Iraq | JSON | 🇮🇱 Israel | JSON | 🇯🇵 Japan | JSON |
| 🇯🇴 Jordan | JSON | 🇰🇿 Kazakhstan | JSON | 🇰🇵 Korea, Dem. People's Rep. | JSON |
| 🇰🇷 Korea, Rep. | JSON | 🇰🇼 Kuwait | JSON | 🇰🇬 Kyrgyz Republic | JSON |
| 🇱🇦 Lao PDR | JSON | 🇱🇧 Lebanon | JSON | 🇲🇴 Macao SAR, China | JSON |
| 🇲🇾 Malaysia | JSON | 🇲🇻 Maldives | JSON | 🇲🇳 Mongolia | JSON |
| 🇲🇲 Myanmar | JSON | 🇳🇵 Nepal | JSON | 🇴🇲 Oman | JSON |
| 🇵🇰 Pakistan | JSON | 🇵🇭 Philippines | JSON | 🇶🇦 Qatar | JSON |
| 🇸🇦 Saudi Arabia | JSON | 🇸🇬 Singapore | JSON | 🇱🇰 Sri Lanka | JSON |
| 🇸🇾 Syrian Arab Republic | JSON | 🇹🇯 Tajikistan | JSON | 🇹🇭 Thailand | JSON |
| Timor-Leste | JSON | 🇹🇷 Turkey | JSON | 🇹🇲 Turkmenistan | JSON |
| 🇦🇪 United Arab Emirates | JSON | 🇺🇿 Uzbekistan | JSON | 🇻🇳 Vietnam | JSON |
| 🇵🇸 West Bank and Gaza | JSON | 🇾🇪 Yemen | JSON |
🌍 Europe
| Country | Data Link | Country | Data Link | Country | Data Link |
|---|---|---|---|---|---|
| 🇦🇱 Albania | JSON | 🇦🇩 Andorra | JSON | 🇦🇹 Austria | JSON |
| 🇧🇾 Belarus | JSON | 🇧🇪 Belgium | JSON | 🇧🇦 Bosnia and Herzegovina | JSON |
| 🇧🇬 Bulgaria | JSON | 🇬🇬 Channel Islands | JSON | 🇭🇷 Croatia | JSON |
| 🇨🇿 Czechia | JSON | 🇩🇰 Denmark | JSON | 🇪🇪 Estonia | JSON |
| 🇫🇴 Faroe Islands | JSON | 🇫🇮 Finland | JSON | 🇫🇷 France | JSON |
| 🇩🇪 Germany | JSON | 🇬🇮 Gibraltar | JSON | 🇬🇷 Greece | JSON |
| 🇬🇱 Greenland | JSON | 🇭🇺 Hungary | JSON | 🇮🇸 Iceland | JSON |
| 🇮🇪 Ireland | JSON | 🇮🇲 Isle of Man | JSON | 🇮🇹 Italy | JSON |
| 🇽🇰 Kosovo | JSON | 🇱🇻 Latvia | JSON | 🇱🇮 Liechtenstein | JSON |
| 🇱🇹 Lithuania | JSON | 🇱🇺 Luxembourg | JSON | 🇲🇹 Malta | JSON |
| 🇲🇩 Moldova | JSON | 🇲🇨 Monaco | JSON | 🇲🇪 Montenegro | JSON |
| 🇳🇱 Netherlands | JSON | 🇲🇰 North Macedonia | JSON | 🇳🇴 Norway | JSON |
| 🇵🇱 Poland | JSON | 🇵🇹 Portugal | JSON | 🇷🇴 Romania | JSON |
| 🇷🇺 Russian Federation | JSON | 🇸🇲 San Marino | JSON | 🇷🇸 Serbia | JSON |
| 🇸🇰 Slovak Republic | JSON | 🇸🇮 Slovenia | JSON | 🇪🇸 Spain | JSON |
| 🇸🇪 Sweden | JSON | 🇨🇭 Switzerland | JSON | 🇺🇦 Ukraine | JSON |
| 🇬🇧 United Kingdom | JSON |
🌎 North America
| Country | Data Link | Country | Data Link | Country | Data Link |
|---|---|---|---|---|---|
| 🇦🇬 Antigua and Barbuda | JSON | 🇦🇼 Aruba | JSON | 🇧🇸 Bahamas | JSON |
| 🇧🇧 Barbados | JSON | 🇧🇿 Belize | JSON | 🇧🇲 Bermuda | JSON |
| 🇻🇬 British Virgin Islands | JSON | 🇨🇦 Canada | JSON | 🇰🇾 Cayman Islands | JSON |
| 🇨🇷 Costa Rica | JSON | 🇨🇺 Cuba | JSON | 🇨🇼 Curacao | JSON |
| 🇩🇲 Dominica | JSON | 🇩🇴 Dominican Republic | JSON | 🇸🇻 El Salvador | JSON |
| 🇬🇩 Grenada | JSON | 🇬🇹 Guatemala | JSON | 🇭🇹 Haiti | JSON |
| 🇭🇳 Honduras | JSON | 🇯🇲 Jamaica | JSON | 🇲🇽 Mexico | JSON |
| 🇳🇮 Nicaragua | JSON | 🇵🇦 Panama | JSON | 🇵🇷 Puerto Rico | JSON |
| 🇸🇽 Sint Maarten (Dutch part) | JSON | 🇰🇳 St. Kitts and Nevis | JSON | 🇱🇨 St. Lucia | JSON |
| 🇲🇫 St. Martin (French part) | JSON | 🇻🇨 St. Vincent and the Grenadines | JSON | 🇹🇹 Trinidad and Tobago | JSON |
| 🇹🇨 Turks and Caicos Islands | JSON | 🇺🇸 United States | JSON | 🇻🇮 Virgin Islands (U.S.) | JSON |
🌏 Oceania
| Country | Data Link | Country | Data Link | Country | Data Link |
|---|---|---|---|---|---|
| 🇦🇸 American Samoa | JSON | 🇦🇺 Australia | JSON | 🇫🇯 Fiji | JSON |
| 🇵🇫 French Polynesia | JSON | 🇬🇺 Guam | JSON | 🇰🇮 Kiribati | JSON |
| 🇲🇭 Marshall Islands | JSON | 🇫🇲 Micronesia, Fed. Sts. | JSON | 🇳🇷 Nauru | JSON |
| 🇳🇨 New Caledonia | JSON | 🇳🇿 New Zealand | JSON | 🇲🇵 Northern Mariana Islands | JSON |
| 🇵🇼 Palau | JSON | 🇵🇬 Papua New Guinea | JSON | 🇼🇸 Samoa | JSON |
| 🇸🇧 Solomon Islands | JSON | 🇹🇴 Tonga | JSON | 🇹🇻 Tuvalu | JSON |
| 🇻🇺 Vanuatu | JSON |
🌎 South America
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