Financial Calculator for Ecosystem Services

This repository provides a Python-based tool for valuing ecosystem services using spatial model outputs. It supports unit-to-monetary conversion and integrates land-use specific valuation. The tool is applicable to services such as flood risk mitigation, soil erosion prevention, water purification, and carbon storage.

Features

• Generic Input Support: Accepts raster outputs from different ecosystem service models.
• Flexible Valuation: Supports global and land-use specific conversion factors.
• Financial Indicators: Computes Benefit-Cost Ratio (BCR), Net Present Value (NPV), and Payback Period.
• Spatial Outputs: Produces monetary value maps to visualize benefit distribution.
• Scenario Comparison: Generates charts to compare financial results across interventions.
• Documentation: Includes full technical reference, usage instructions, and examples.

Repository Structure

generic_es_valuation_workflow_repo/
├── README.md
├── src/
│   ├── ecosystem_service_valuation_workflow.py
│   └── test_ecosystem_service_workflow.py
├── docs/
│   ├── fcalc_documentation.md
├── data/
│   ├── sample_baseline_service_provision.tif
│   ├── sample_intervention_A_service_provision.tif
│   ├── sample_intervention_B_service_provision.tif
│   ├── sample_intervention_C_service_provision.tif
│   └── sample_land_use_map.tif
├── results/
│   ├── ecosystem_service_results/
│   │   ├── monetary_value_map_intervention_A.png
│   │   ├── monetary_value_map_intervention_B.png
│   │   ├── monetary_value_map_intervention_C.png
│   │   ├── scenario_comparison.png
│   │   └── financial_summary_report.csv
└── .gitignore

Getting Started

Prerequisites

• Python 3.8+
• numpy
• rasterio
• matplotlib
• seaborn
• pandas

Install dependencies using pip:

pip install numpy rasterio matplotlib seaborn pandas

Running the Workflow

1. Clone the repository:

   git clone https://github.com/Open-Earth-Monitor/fcalc.git
   cd fcalc

2. Place your data: If you have your own ecosystem service model outputs (baseline and intervention rasters) and an optional land-use map, place them in the data/ directory. You can also use the provided sample data.

3. Configure and Run: Open src/ecosystem_service_valuation_workflow.py and modify the if __name__ == "__main__": block to configure your specific scenarios, investment costs, and conversion factors. Then, run the script:

   python src/ecosystem_service_valuation_workflow.py

4. View Results: Outputs (monetary value maps, comparison charts, and a financial summary CSV) will be saved in the results/ecosystem_service_results/ directory.

Running Tests

To run the comprehensive test suite and validate the workflow:

python src/test_ecosystem_service_workflow.py

Documentation

Detailed documentation, including methodology, implementation details, usage guidelines, and results analysis, can be found in the docs/ directory:

• fcalc_documentation.md (Markdown format)

License

This project is licensed under the MIT License - see the LICENSE file for details.