carbon-credit-4-adapt

Analysis repository for assessing technical potential and net economic benefit (NEB) of adaptation-linked carbon crediting in agriculture. Includes spatial workflows, country-level modelling (Kenya, Ethiopia, Nigeria), and reproducible outputs for evidence-based decision-making.

Purpose

The analysis supports the development of country-specific evidence on where agricultural practices may generate:

• mitigation benefits
• productivity benefits
• adaptation benefits
• positive net economic benefit
• plausible opportunities for scaling

The initial country focus is:

• Ethiopia
• Kenya
• Nigeria

The analytical framework is designed to support country profiling and related spatial outputs, including maps, tables, and summary datasets.

Scope of this repository

This repository is used to:

• prepare and harmonize spatial input layers
• generate technical potential surfaces
• estimate NEB inputs and outputs
• run sensitivity and uncertainty analyses
• produce intermediate and final analytical outputs
• document methods, assumptions, and data dependencies

This repository does not contain:

• project management materials
• meeting administration
• donor reporting files
• communications assets
• production deployment code for a tool or web platform

If a deployable tool is developed, that should be maintained in a separate repository.

Analytical components

The analysis is expected to cover five broad components:

1. Setup and harmonization

   • common grid
   • practice-by-data matrix
   • baseline layers
   • country-level analytical specifications

2. Technical potential

   • mitigation surfaces
   • productivity benefit estimation
   • adaptation benefit estimation
   • spatial integration and opportunity zoning
   • uncertainty analysis

3. Net Economic Benefit (NEB)

   • framework definition
   • cost estimation
   • adoption assumptions
   • spatial NEB calculation
   • sensitivity analysis

4. Scaling and interpretation

   • alignment with feasibility and scaling considerations
   • scenario exploration
   • country-level synthesis

5. Outputs

   • maps
   • summary tables
   • country-level analytical datasets
   • documented assumptions and metadata

Repository structure

├── README.md ├── data/ │ ├── raw/ │ ├── interim/ │ ├── processed/ │ └── metadata/ ├── scripts/ │ ├── 01_setup/ │ ├── 02_technical_potential/ │ ├── 03_neb/ │ ├── 04_scaling/ │ ├── 05_outputs/ │ └── utils/ ├── config/ │ ├── countries/ │ ├── practices/ │ ├── scenarios/ │ └── paths/ ├── outputs/ │ ├── figures/ │ ├── tables/ │ ├── rasters/ │ └── summaries/ ├── docs/ │ ├── methods/ │ ├── data_sources/ │ └── schemas/ └── tests/

Working principles

• Reproducibility first: all core outputs must be generated from code and configuration
• No large raw data in the repository
• Modular workflows across technical potential, NEB, and outputs
• Centralised configuration (avoid hard-coded parameters and paths)
• All key assumptions must be documented

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Data handling

• Large spatial and raw datasets are not stored in this repository
• External storage should be used for heavy data assets
• This repository maintains:
  • metadata
  • data inventories
  • access instructions
  • processing scripts
• All data sources must be documented in docs/data_sources/

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Branching model

• main – stable, reviewed code
• Feature branches for development (e.g. feature/neb, feature/ethiopia)
• Pull requests required before merging into main
• Keep branches short-lived and focused

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Environment

• The computational environment must be defined using one approach:
  • renv.lock (R)
  • environment.yml (conda)
  • requirements.txt (Python)
• All contributors should use the same environment specification

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Outputs

Typical outputs include:

• Spatial layers (e.g. mitigation, adaptation, NEB)
• Maps and visualisations
• Summary tables
• Country-level datasets
• Intermediate analytical products

All outputs should have:

• Clear structure
• Consistent naming
• Version or date reference

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Status and ownership

• This repository is under active development
• Methods and structure may evolve during early phases
• Maintainers:
  • Pete Steward
  • Ani Ghosh
  • Chun Song