ClimaCore

ESG Sensitivity and Local Engagement for Renewable Energy Projects

Overview

This repository focuses on ESG (Environmental, Social, and Governance) sensitivity and local engagement to evaluate and enhance the success of renewable energy projects. It introduces a dynamic ESG scoring framework and tools to analyze various factors, such as resources, government, location, local sentiment, and future risk analysis.

Features

• Dynamic ESG Scoring Framework (1–100):

  • Evaluate projects based on factors like resources, government, and location.
  • Incorporate social media sentiment analysis and economic impact evaluation.
  • Assess biodiversity and soil suitability for project recommendations.

• Alternative Project Locations:

  • Identify regions with optimal scores or better project viability.
  • Suggest alternative renewable energy projects based on local conditions.

• Real-Time Scoring and Monitoring:

  • Dynamically calculate and update ESG scores based on live data.
  • Provide interactive dashboards for stakeholders.

• Future Risk Analysis:

  • Predict potential risks, such as policy changes or environmental degradation.
  • Use machine learning models for forecasting and geospatial analysis for optimization.

Project Structure

The repository contains the following key files:

Core Files

1. backup.py
   Backup and recovery scripts for ESG scoring data.

2. esg_calculator.py
   Core module for computing ESG scores based on predefined factors.

3. for_test_data.py
   Test data generation and validation for ESG scoring models.

4. get_bio_diversity_data.py
   Module to fetch and analyze biodiversity data for project locations.

5. GoogleScrapper.py
   Script to scrape relevant data from Google for ESG analysis.

6. LatestSoilData.py
   Tool to analyze soil properties and determine site suitability.

7. new.py
   Utility functions and additional ESG computations.

Additional Modules

1. single.py
   Functions for handling single project ESG analysis.

2. t.py
   Helper utilities for testing and debugging.

3. TextSummrizer.py
   Summarizes textual data for quick insights into local sentiment and project documentation.

4. updated.py
   Contains updated implementations of ESG scoring algorithms.

5. WindSpeed.py
   Analyzes wind speed data for assessing renewable energy project feasibility.

Installation

To set up the project, clone this repository and install the required dependencies:

# Clone the repository
git clone https://github.com/username/esg-project.git

# Navigate to the project directory
cd esg-project

# Install dependencies
pip install -r requirements.txt