Climate Action Prioritization (CAP) Tool

This is a multi-stage tool for generating climate action priorities for cities.

License

This project is licensed under the Affero General Public License v3.0. See the LICENSE file for details.

Directories

• data/: Contains data files used by the different stages.
• schema/: Contains JSON schemas for the data files.
• extractor/: Extracts action data and generates a JSON file.
• prioritizer/: Prioritizes actions based on the extracted data.
• scripts/: Contains helper scripts like adding ccras, ghgis or uploading files to AWS S3. This folder also contains a run_pipeline script which automatically prioritizes all actions inside the long list of actions against a chosen city, transforms the data to match the required frontend schema and uploads the created file to the AWS S3 bucket for updating the frontend.

Usage

Data Import

We are importing data from several sources:

1. City context data from: "https://ccglobal.openearth.dev/api/v0/city_context/city"
2. City CCRA data from: "https://ccglobal.openearth.dev/api/v0/ccra/risk_assessment/city"
3. City GHGI data from exported .csv files from CityCatalyst inventories

We have a file with all the city locodes we want to import: data/cities/brazil_city_locodes.json

There are 3 utilities files to import all the data:

To avoid errors, run them in the given sequence:

1. python scripts/create_city_data/run_context_bulk_import.py --bulk_file data/cities/brazil_city_locodes.json
2. python scripts/create_city_data/run_ghgi_bulk_import.py --bulk_file data/cities/brazil_city_locodes.json
3. python scripts/create_city_data/run_ccra_bulk_import.py --bulk_file data/cities/brazil_city_locodes.json

This will import all the city data and store inside data/cities/city_data.json.

Automated pipeline for ranking and bucket upload

Run the run_pipeline.py for a single city or run_pipeline_bulk.py for bulk processing of all cities listed in data/cities/brazil_city_locodes.json.

These scripts will automatically create the ranked actions for this city (or all cities), format and upload those actions to the AWS S3 bucket for displaying in the frontend app.

Example: Single file: python -m scripts.upload_to_frontend.run_pipeline --locode "BR BHZ"

Bulk: python -m scripts.upload_to_frontend.run_pipeline_bulk --bulk_file data/cities/brazil_city_locodes.json --workers 10

• Workers are the paralell thread to use for bulk processing (default: number of cores)

Requirements

To run this pipeline, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY must be set in an .env file with permissions to write to the S3 bucket /data folder.

To run the script, the packages inside /extractor/requirements.txt needs to be installed. The script will be looking for a virtual environment .venv inside the /.venv top level folder.

If you create the virtual environment with a different name then .venv the path inside run_pipeline.sh must be adjusted accordingly.

Under Windows OS, this script is best executed under WSL oder Git bash.

Debugging

Langsmith

If the LLM logs should be logged by langsmith, the .env variables must be set LANGCHAIN_TRACING_V2=true as well as the corresponding project name and api key.