California Climate Investments (CCI) Analysis

A data analysis project examining collaboration patterns in California's climate funding programs and their impact on greenhouse gas (GHG) reduction efficiency and equity outcomes — particularly in disadvantaged communities (DACs).

Overview

This project analyzes the California Climate Investments dataset to answer questions like:

How does multi-agency collaboration affect program effectiveness?
What are the regional variations in climate investment patterns?
How do EV voucher/rebate programs (CARB) compare to other programs?
Where do GHG efficiency and equity goals align or conflict?
How have collaboration and funding patterns changed over time?

The dataset covers 146,305 projects across 21 agencies and 39 programs, representing $11.59 billion in total funding and 112.7 million metric tons of GHG reductions.

Project Structure

california-equity-git/
├── run_cci_analysis.py                 # Main entry point — orchestrates full workflow
├── cci_analyzer.py                     # CCIDataAnalyzer class (data loading/cleaning)
├── cci_collaboration_analysis.py       # Collaboration pattern analysis
├── research_analysis_script.py         # Research question analysis
├── regional_analysis_script.py         # Regional distribution analysis
├── collaboration_detection_script.py   # Collaboration pattern detection
├── data_cleaning_script.py             # Data cleaning utilities
├── 01_analyzer.ipynb                   # Interactive Jupyter notebook
│
├── data/
│   └── cci_programs_data_reduced.csv   # Processed dataset (~40MB)
├── data_raw/
│   └── cci_programs_data.csv           # Original CCI dataset (~242MB)
│
├── california_enviroscreen/            # CalEnviroScreen 4.0 data (geodatabase + shapefiles)
├── assembly_district_shapefile/        # CA State Assembly Districts 2020 shapefiles
│
└── output/                             # Generated analysis outputs and visualizations

Tech Stack

Python 3.12+
pandas / numpy — data manipulation
matplotlib / seaborn — visualization
geopandas / shapely / pyproj — geospatial analysis
scipy — statistical testing
scikit-learn — data preprocessing

Setup

# Clone the repo
git clone <repo-url>
cd california-equity-git

# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install pandas numpy matplotlib seaborn geopandas shapely pyproj scipy scikit-learn

Usage

Run the full analysis pipeline:

python run_cci_analysis.py --data_path data/cci_programs_data_reduced.csv --output_dir output

Optional flags:

Flag	Description
`--skip_cleaning`	Skip data cleaning step (use pre-cleaned data)
`--skip_analysis`	Skip collaboration analysis step
`--skip_research`	Skip research question analysis

Pipeline stages:

Data cleaning — standardizes columns, parses dates, extracts coordinates, calculates derived metrics
Collaboration analysis — identifies multi-agency programs and collaboration patterns
Research analysis — answers core research questions with statistical tests and visualizations

Output files (charts, CSVs, summaries) are written to the --output_dir directory.

Data

Primary Dataset

Source: California Climate Investments Open Data Portal
File: data_raw/cci_programs_data.csv (242MB) / data/cci_programs_data_reduced.csv (40MB processed)

Supplementary Data

CalEnviroScreen 4.0 — used to identify and score disadvantaged communities (DACs)
CA State Assembly Districts 2020 — shapefiles for regional geographic analysis

Note: Large raw data files and shapefiles are tracked with Git LFS.

Key Findings (from data summary)

Metric	Value
Total projects	146,305
Total funding	$11.59 billion
GHG reductions	112.7 million metric tons
CARB projects	125,581 (85.8%)
EV voucher projects	109,270 (74.7%)
Median GHG efficiency	$312.5 / metric ton
Avg. DAC benefit multiplier	1.3x

License

This project is for research and educational purposes. The underlying CCI data is publicly available from the California Air Resources Board.

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