---
license: apache-2.0
language:
- en
tags:
- climate
pretty_name: IFVI Global Value Factors Database, Data Analysis Refactor, V2
size_categories:
- 100K<n<1M
---

![alt text](images/banner.jpg)


Refactor and HF dataset (including texts): Daniel Rosehill

Source data: International Foundation for Valuing Impacts 

![alt text](images/section-breaks/1.png) 

This dataset provides V2 of a refactoring of the Global Value Factor Database (GVFD) by the International Foundation for Valuing Impacts intended to enhance the original dataset for machine readability and integration into data analysis and visualization workloads. 

 ![alt text](images/section-breaks/2.png)

The International Foundation for Valuing Impacts (IFVI) produces an (open-source) database called the Global Value Factor Database. 

This database provides an array of "value factors". 

Value factors are multipliers whose purpose is to convert companies' non-financial "impacts" (across the social and environmental realms) into units of currency. For standardization, all value factors convert into US Dollars. To localize them in non-USD jurisdictions, users can simply apply currency conversion after this initial calculation.

![alt text](images/4.png)

---

![alt text](images/section-breaks/3.png)


Various global frameworks govern how companies practice accounting - the most important of which are IFRS and GAAP. These standard-setters do not (themselves) prescribe the mandate to use their methods. But their application is mandated by governments and multilaterals (who may wish to use them to standardize accounting practices within a geopolitical bloc, for instance).

IFRS, for example, is used in more than 140 countries worldwide with an estimated 30-35,000 companies subject to its directives. Trillions of dollars are computed according to its directives and its definition of value. 

As much as accounting rules seem like an unlikely subject for philosophical speculation, peel back the surface and you'll find a Pandora's box of questions which impact accounting helps to explore. 

If nothing else, impact accounting allows us to model a financial system in which companies' environmental and social impacts were integrated into financial reporting. By doing so, we can answer questions like: "if this were the system ... how many companies would actually be profitable at all? And which wouldn't?"

"Money" - the ultimate arbiter of value in the business world - is itself an abstraction; a yardstick created by society so that cruder systems of wealth-reckoning could be replaced by one fit for the modern world. Businesses are collectives of humans which seek to improve their lives through aggregating wealth. But why should that system account only for *their* welfare and not that of those potentially impacted by their pursuit of profit?

Companies, like people, have effects that extend far beyond generating profit for shareholders (as determined by the rules which we prescribe for those calculations) and paying out money through activities like employing workers and buying goods and services from suppliers. 

They can improve the welfare of surrounding communities through that employment; entrench socioeconomic inequality through unfair labor practices; pollute water systems; remediate pollution in their communities. 

Under normative accounting rules, a company may make enormous profits while paying scant or no attention to its environmental performance. It can generate pollution which impacts the poorest communities in far-removed parts of the world. 

This happens because our societies' response to "that isn't right" has, so far, been to create a parallel mechanism of sorts for reckoning these non-financial impacts. 

This has taken the form of mechanisms like ESG. Companies are urged to measure their performance in important respects like climate performance. But the metrics which measure that performance remain ring-fenced out of bottom-line financial calculations. 

Impact accounting argues that in order to integrate companies' financial performance with their more holistic impacts (affecting people and planet), those impacts need to be translated into equivalent terms (money) and then valued in a harmonized set of accounts. 

The argued benefits of this system:

- Companies have a material incentive to be better environmental performers 
- Conversely, companies which cause harm to these external groups will see their financial performance more closely track their aggregate "value" to the world at large 

In essence, impact accounting attempts to shift the viewpoint through which companies are valued from the self-serving one (how much money did we make or lose) to a 'global' one (how much money did we make or lose - offset by the proxied total cost of our net impacts to society and planet). 

---

![alt text](images/5.png)

For impact accounting to evolve into a viable practice that could eventually succeed traditional accounting, it must be standardized on multiple levels. Standardization is needed both in its application and in the precise mathematical methods used to translate companies’ environmental and social impacts into monetary values. This translation allows such impacts to be compared directly—on a like-for-like basis—with established financial reporting standards. In effect, the process involves converting raw data about impacts into monetary terms.

The Global Value Factor Database (GVFD) addresses the climate component of impact accounting, a domain where quantification is comparatively more straightforward. This approach is not without precedent. A well-known example is the social cost of carbon, which the IFVI dataset places at $236 per metric tonne of CO₂. Various organizations have proposed different values for this figure at different times, drawing on evolving scientific consensus.

The GVFD can be understood as an attempt to provide similar single reference values—not just for greenhouse gas emissions but for a wide range of climate-related impacts. The scope of this task is substantial. To manage it, environmental impacts are organized into categories, including:

- Air pollution
- Land use and conservation
- Waste generation
- Water consumption
- Water pollution

Among these, the water pollution value factors stand out for their sheer number of "line items", reflecting the wide variety of chemicals that companies may release into water systems. 

By contrast, other categories are stratified differently: air pollution is broken down by pollutant type, land use by type of displacement, waste by disposal method, and water consumption by the displacement effect it causes.

Each methodology underlying these calculations is supported by a substantial body of research and documented in detail by IFVI, accompanied by a user manual. Those consulting this derivative dataset should specifically refer to Version 2 of the IFVI release, which contains the relevant methodological framework.

## Quick Links

- **[Usage Guide](Usage.md)** - How to use the value factors for financial impact modeling
- **[Contact Information](contact.md)** - Get in touch with questions or feedback

---

![alt text](images/6.png)

In late 2024, the International Foundation for Valuing Impacts (IFVI) released the **Global Value Factors Database (GVFD)**. This pioneering dataset provides a framework for converting non-traditional impacts into financial terms, offering a new lens for evaluating global value creation. For the full scope, methodology, and theoretical underpinnings, I encourage readers to consult the official IFVI website and accompanying documentation.

This document presents an overview of my **independent refactoring effort**, undertaken to make the GVFD more accessible for data analysis, machine learning, and visualization workflows. The IFVI kindly granted consent for the republication of their data in this format. However, I take sole responsibility for any errors introduced in the refactoring process.

The dataset continues to be governed by IFVI’s terms of use, as set out in the original publication (available at [ifvi.org](https://ifvi.org)). My work here is based on the **first release** of the GVFD. Should IFVI publish subsequent versions, they supersede the dataset refactored in this project.

![alt text](images/7.png)


No substantive changes were made to IFVI’s data values or methodologies. However, I applied several light-touch edits to improve usability and consistency:


 
## Dataset Access Links

These links are to the roots of the refactored dataset on Hugging Face and are provided for ease of navigation and access:

**📖 [User Guide (PDF)](user-guide.pdf)** - Guide to using this dataset including potential use cases to explore

| Category | Description | Link |
|----------|-------------|------|
| **Root Directories** | | |
| Refactored Dataset Root | Main refactored data directory | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored) |
| **CSV Format** | | |
| CSV Root | All CSV formatted data | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/csv) |
| Combined Value Factors | All value factors in combined CSV files | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/csv/combined) |
| Individual Value Factors | Separate CSV files by value factor | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/csv/value-factors-separate) |
| **JSON Format** | | |
| JSON Root | All JSON formatted data | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/json) |
| Combined JSON | All value factors in combined JSON files | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/json/combined) |
| Individual JSON VFs | Separate JSON files by value factor | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/json/individual-vfs) |
| Per Country | JSON data organized by country | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/json/per-country) |
| US States | JSON data for US states specifically | [View](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2/tree/main/refactored/json/per-country/us-states) |

## Direct File Links

For direct access to individual data files:

### CSV Files

| Category | Description | Direct Link |
|----------|-------------|-------------|
| **All Value Factors** | Complete dataset in CSV format | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/csv/combined/combined.csv) |
| **Air Pollution** | Air pollution value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/csv/value-factors-separate/air-pollution.csv) |
| **Land Use** | Land use value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/csv/value-factors-separate/land-use.csv) |
| **Waste** | Waste management value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/csv/value-factors-separate/waste.csv) |
| **Water Consumption** | Water consumption value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/csv/value-factors-separate/water-consumption.csv) |
| **Water Pollution** | Water pollution value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/csv/value-factors-separate/water-pollution.csv) |

### JSON Files

| Category | Description | Direct Link |
|----------|-------------|-------------|
| **All Value Factors** | Complete dataset in JSON format | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/json/combined/combined-value-factors.json) |
| **Air Pollution** | Air pollution value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/json/individual-vfs/air-pollution.json) |
| **Land Use** | Land use value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/json/individual-vfs/land-use.json) |
| **Waste** | Waste management value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/json/individual-vfs/waste.json) |
| **Water Consumption** | Water consumption value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/json/individual-vfs/water-consumption.json) |
| **Water Pollution** | Water pollution value factors | [Download](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2/blob/main/refactored/json/individual-vfs/water-pollution.json) |

---

# Dataset Parameters

## Decimal Value Occurrence

These parameters are provided to assist with selecting a decimal value for SQL ingestion.  A four decimal float is strongly advised (65% of values).

| Decimal Places | Count | Percentage |
|----------------|-------|------------|
| 0 (Integers) | 10,062 | 9.62% |
| 1 | 2,055 | 1.97% |
| 2 | 18,698 | 17.88% |
| 3 | 6,260 | 5.99% |
| 4 | 67,489 | 64.54% |

## Geoparameters

| Metric | Count | Percentage |
|--------|-------|------------|
| **Total unique geolocations** | 268 | 100.0% |
| **Entities with ISO 3166-1 codes** | 195 | 72.8% |
| **US states (with state codes)** | 50 | 18.7% |
| **Non-sovereign entities (no ISO)** | 23 | 8.6% |

---

## Use-Case Suggestions

| Use Case             | Description                                                                                                                                           |
|-----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| AI Tools             | The JSON array is machine-readable and ideal for ingestion into vector databases for RAG and AI use. A chatbot implementation has been validated.      |
| Data Visualisation   | The data is suitable for data visualisation and geovisualisation.                                                                                      |
| Policy Modelling     | Pair the value factors with any geo-labelled dataset (e.g., UN Human Development Index) to map potential effects of impact accounting by policy groups. |
| Hugging Face Projects| The dataset can be directly ingested into Hugging Face Spaces built with Gradio and Streamlit.                                                         |
| Calculators          | Can be paired with calculators to provide impact accounting estimates (ensuring consistent units between source data and value factors).                |
| Correlation Analysis | Pair the value factors with current or historical environmental data to identify correlations between financial and environmental performance.          |


---

## Thanks, Credits

The GVFD represents a milestone in the effort to measure and assign value to non-traditional impacts. My **Data Analysis Refactoring V2** initiative is intended to extend the usability of IFVI’s work by making the dataset friendlier for modern analysis pipelines.

I am grateful to IFVI for granting permission to republish the data in this restructured form. Any inaccuracies or errors in the refactoring are solely my own.

---

## Source Data


- Excel workbook containing multiple sheets with environmental impact data
- Complex tabular format with countries as columns and impact categories as rows
- Contains metadata rows for ISO codes and regional classifications


## Output Data Formats

### Hierarchical JSON Structure
```json
{
  "countries": {
    "CountryName": {
      "metadata": { "iso_code": "XXX", "region": "Region" },
      "categories": {
        "CategoryName": {
          "regions": {
            "RegionName": {
              "datasets": {
                "DatasetName": {
                  "values": [...],
                  "statistics": { "mean": X, "min": Y, "max": Z }
                }
              }
            }
          }
        }
      }
    }
  }
}
```

### Compact JSON Structure
```json
[
  {
    "country": "CountryName",
    "iso_code": "XXX",
    "region": "Region",
    "category": "CategoryName", 
    "subcategory": "SubcategoryName",
    "dataset": "DatasetName",
    "value": 123.45
  }
]
```

## Repository Structure

```
IFVI-GVFD-0825/
├── source/                          # Original Excel file
├── data/
│   ├── individual-sheets/           # Raw CSV extracts
│   ├── cleaned-sheets/              # Standardized CSVs
│   ├── json/                        # Primary JSON outputs
│   └── country-analysis/            # Country-level aggregations
├── processing.md                    # This documentation
├── README.md                        # Repository overview
└── [processing scripts]             # Python conversion scripts
```

## Processing Statistics

- **Total Countries:** 229
- **Countries with ISO Codes:** 205 (89.5%)
- **Environmental Categories:** 5 (air pollution, GHGs, land use, waste, water)
- **Total Data Points:** ~115,000 individual measurements
- **Regional Coverage:** 7 major world regions
- **File Size Reduction:** ~4.5MB compressed JSON vs. original Excel format

## Data Validation

### ISO Code Validation
- Cross-referenced country names with standard ISO 3166-1 alpha-3 codes
- Manual verification of 40+ country mappings
- 89.5% coverage with valid ISO codes

### Data Completeness
- Water consumption: Limited coverage (11 countries)
- Other categories: Near-complete coverage (216-217 countries each)
- No missing value imputation performed (preserves data integrity)

### Regional Distribution Validation
- Verified regional classifications against World Bank standards
- Identified and flagged anomalous regional values for review

## Usage Recommendations

### For Analysis
- Use `ifvi_environmental_data_compact.json` for data science workflows
- Use `countries_aggregated.json` for country-level comparisons
- Reference `iso_code_mapping.csv` for geographic joins

### For Applications
- Hierarchical JSON provides nested access patterns
- Compact JSON enables fast loading and filtering
- Country analysis files support dashboard and visualization needs

## Future Enhancements

- Automated ISO code validation against external APIs
- Time series analysis if historical data becomes available
- Data quality scoring and completeness metrics
- Integration with additional environmental datasets