๐ Industrial Carbon Emission Analysis
This project analyzes and visualizes industrial carbon emissions based on simulated data. The goal is to explore how different industries contribute to carbon output and how energy efficiency relates to production volume.
๐ Project Overview
- Topic: Carbon Emission Analysis in Industrial Sectors
- Level: Beginner
- Tools Used: Python, Pandas, Matplotlib, Seaborn, SQL
- Focus Area: Environmental Engineering, Technology
- Dataset: Synthetic dataset generated to simulate real-world industrial emission patterns
๐ Dataset Description
The dataset includes 500 rows of synthetic data representing:
Column Name |
Description |
Industry |
Industry type (e.g., Cement, Steel, Textile) |
Year |
Observation year (2010โ2023) |
Energy_Consumption_MWh |
Energy consumed in megawatt-hours |
Production_Units |
Total production volume |
Emission_Factor |
Emission factor per MWh (kg CO2/MWh) |
CO2_Emissions_kg |
Total CO2 emissions in kilograms |
CO2_per_Unit |
Emission per production unit (kg/unit) |
๐ฏ Business Questions
- Which industries contribute the most to carbon emissions?
- How does energy consumption relate to production efficiency?
- Which industries have the best CO2 per unit efficiency?
- What trends are visible in emissions over time?
๐ Key Insights
- Emission-heavy sectors like Energy and Cement have high CO2 output per unit.
- Some industries show increasing emissions despite stable production.
- Emission efficiency (CO2 per unit) varies significantly by industry, indicating optimization opportunities.
- Python for data processing and visualization
- Pandas for data wrangling
- Matplotlib & Seaborn for visualization
- SQL (optional) for exploratory queries on tabular format
๐งช Sample Analysis
- Grouped emissions by industry and year
- Calculated efficiency ratios and visualized outliers
- Created bar plots and line graphs to highlight trends
๐ File Structure