JessNaaFrance/ghana-climate-finance-model

R/Shiny tool for exploring Ghana’s mitigation climate finance gap. Includes a reproducible data-cleaning pipeline, processed CPI-based finance inputs, scenario analysis through 2035, and dashboard screenshots for policy-oriented climate finance modeling. # Ghana Climate Finance Gap Explorer ## Overview This repository contains code for an Environmental Defense Fund climate finance project focused on Ghana. It combines a reproducible R data-cleaning workflow with an R Shiny scenario tool that projects Ghana’s mitigation climate finance gap under alternative public finance, private finance, leverage, and carbon market assumptions. ## Research purpose The goal of the project is to estimate and visualize how far existing and projected mitigation finance flows can go toward meeting Ghana’s 2030 climate finance needs. The app allows users to compare business-as-usual and policy scenarios through 2035. ## Repository structure - `data_cleaning.R` cleans and aggregates Ghana’s climate finance data into a mitigation-only annual dataset. - `app.R` runs the interactive Shiny dashboard. - `data/raw/` contains the original Ghana finance input file. - `data/processed/` contains the cleaned dataset used by the app. ## Data The raw input comes from the Climate Policy Initiative dataset filtered to Ghana by recipient country. Public finance includes loans, grants, and guarantees, while private finance is measured using equity flows. ## Methodology The cleaning pipeline aggregates annual flows by climate class, separates adaptation and mitigation finance, and allocates private mitigation finance proportionally using the public mitigation share as a proxy. The Shiny application then projects future public and private flows using user-defined growth rates and calculates the remaining finance gap under different assumptions about leverage and carbon market mobilization. ## Running the app 1. Open `data_cleaning.R` and run it to create the processed mitigation-only dataset. 2. Ensure the cleaned file is saved in `data/processed/`. 3. Open `app.R`. 4. Run the app in RStudio or with: `shiny::runApp()` ## Key assumptions - Mitigation finance need is based on a mitigation share parameter derived from the CPI Africa report. - Public and …