🌍 ESG & Financial Performance Analysis

📘 Project Overview

This project explores the intricate relationship between Financial Performance and Environmental, Social, and Governance (ESG) metrics across various companies, industries, and regions over a decade.

The core objective is to move beyond correlation and provide actionable insights and strategic recommendations that guide corporate leaders toward better sustainability practices—specifically focusing on how to cut emissions and manage resource usage to simultaneously boost ESG scores and enhance financial valuation.

Key Areas of Analysis:

• Financial Metrics: Revenue, MarketCap, GrowthRate, Profit.
• Sustainability Indicators: Carbon Emissions, Energy Consumption, Water Usage, Emissions per Revenue.
• ESG Performance: ESG_Overall, ESG_Environmental, ESG_Social, ESG_Governance.

📂 Repository Structure

├── code.py                           \# Main Python analysis code  
├── company\_esg\_financial\_dataset.csv   \# Dataset used in analysis  
├── plots/                            \# Generated visualizations  
│   └── plots/                        \# Subfolder with saved plots  
├── CodeCrafters\_Round1.pptx          \# Presentation of insights & recommendations  
├── requirements.txt                  \# Project dependencies  
└── README.md                         \# Project documentation

📄 Dataset Details

Source: Company-level dataset (financial + ESG metrics + sustainability usage). Granularity: Records by company, industry, region, and year. Variables included:

• 📊 Financial: Revenue, Profit, GrowthRate, MarketCap
• 🌱 ESG: ESG_Overall, ESG_Environmental, ESG_Social, ESG_Governance
• 🔋 Sustainability: CarbonEmissions, EnergyConsumption, WaterUsage, Emissions per Revenue

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🛠️ Project Execution

• ✅ Performed data cleaning, feature engineering (Profit, Emissions per Revenue).
• ✅ Conducted 11 key EDA questions covering multivariate and time-series analysis.
• ✅ Built stream-structured, stakeholder-friendly plots (line charts, bar charts, scatter plots, heatmaps).
• ✅ Extracted 7 key insights with real-world sustainability implications.
• ✅ Proposed 3 policy recommendations for companies & regions.

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/aaagairi/esg-financial-analysis.git cd esg-financial-analysis

2. Install dependencies:

   pip install -r requirements.txt

3. Run the analysis:

   python code.py

4. View results:

   • 📈 Plots: Saved inside the plots/ directory.
   • 🖼️ Presentation: Open CodeCrafters_Round1.pptx.
   • 📄 Dataset: Available at company_esg_financial_dataset.csv.