🌍 **Dataset Description: Bioenergy Modelling BY Yeasmin **

This dataset provides essential information for modeling greenhouse gas (GHG) emissions in diesel-dependent communities transitioning to bioenergy. The goal is to evaluate the environmental impacts, including emission reductions and carbon sequestration, through data-driven analysis.

---

📂 Dataset Details

File Name	Bioenergy_Modelling_Dataset.csv
Dataset Size	Contains rows of data with several numerical and categorical features related to GHG modeling.
Format	CSV (Comma-Separated Values)

---

🔑 Key Columns and Descriptions

Column Name	Description	Type
fuel_type	Type of fuel used (e.g., Diesel, Bioenergy)	Categorical
ghg_emissions	Total GHG emissions in metric tons CO₂-equivalent	Numerical
biomass_growth_rate	Annual growth of biomass in kilograms per hectare	Numerical
decomposition_rate	Percentage rate at which organic biomass decomposes annually	Numerical
carbon_sequestration	Amount of carbon stored in biomass and soil (metric tons)	Numerical
transport_emissions	GHG emissions from transportation (metric tons CO₂-equivalent)	Numerical
scenario_year	The year for which the scenario data is modeled (e.g., 2024)	Numerical
region	Region or community (e.g., Arctic communities)	Categorical

---

📊 Purpose and Use Case

1. Environmental Analysis:

   • Evaluate greenhouse gas (GHG) reductions achieved by transitioning from diesel to bioenergy.

2. Scenario Modeling:

   • Examine GHG emissions under different timeframes and adoption scenarios (e.g., short-term vs. long-term impacts).

3. Patterns and Trends:

   • Identify temporal patterns in biomass growth, decomposition, and overall emissions.

4. Hypothesis Testing:

   • Compare emissions between diesel and bioenergy usage to validate hypotheses about their environmental impacts.

---

🛠️ How to Use the Dataset

1. Load the Dataset:
   Import the dataset into your Python or Jupyter Notebook for analysis:

   import pandas as pd  
   data = pd.read_csv('Bioenergy_Modelling_Dataset.csv')  

2. Data Overview:

   • Check the size, structure, and column types.
   • Display the first few rows using:

     data.head()

3. Perform EDA Steps:

   • Handle missing values, detect outliers, and analyze data distributions.
   • Visualize relationships between variables (e.g., fuel_type vs. ghg_emissions).

4. Visualize Key Insights:

   • Use charts like histograms, scatter plots, and heat maps to highlight trends.

---

🔗 Repository Link

The dataset is stored in the GitHub repository for this project:
GitHub Repository: Greenhouse Gas Modeling

---