This dataset provides a country-level longitudinal time-series analysis of global forest dynamics from 2000 to 2025. It tracks changes in forest area, deforestation and afforestation rates, carbon stock levels, and the primary drivers influencing forest change.

The dataset is synthetic but realistically modeled, inspired by global environmental reporting frameworks such as FAO Forest Resources Assessments, Global Forest Watch methodologies, and IPCC carbon estimation guidelines. It reflects realistic regional patterns, including higher deforestation in tropical regions, relative forest stability in Europe, and wildfire-driven variability in parts of North America and Oceania.

📊 Dataset Overview

Time Coverage: 2000–2025 (26 Years)

Geographic Coverage: Global

Granularity: Country-level annual records

Total Rows: 5,700+

Total Columns: 9

Data Type: Structured time-series dataset

Each row represents one country in one specific year, enabling both longitudinal and cross-sectional analysis.

🧾 Columns Description

Country – Country name (includes real countries and synthetic entries labeled Country_Gen_XXX).

Year – Observation year (2000–2025).

Forest_Area_km2 – Total forest-covered area in square kilometers.

Land_Area_km2 – Total land area of the country (constant over time).

Forest_Cover_Pct – Percentage of land covered by forest.

Annual_Deforestation_Rate (%) – Percentage of forest area lost during the year.

Annual_Afforestation_Rate (%) – Percentage of forest area gained during the year.

Total_Carbon_Stock_Tonnes – Estimated total carbon stored in forest biomass.

Primary_Driver_of_Change – Main factor influencing forest change (e.g., Commercial Agriculture, Logging, Wildfires, Mining, Urban Expansion, Policy Protection, Initial State).

🎯 Ideal Use Cases

Time-Series Forecasting

Carbon Stock Trend Analysis

Environmental Impact Modeling

Regional Forest Comparison

Climate Risk Simulation

Data Cleaning & Visualization Practice

⚠️ Disclaimer:
This dataset is fully synthetic and generated using rule-based modeling techniques to mimic real-world environmental patterns. It is intended for educational, analytical, and prototyping purposes only and should not be used for official reporting or policy decisions.