Comprehensive Data Analysis of Pakistan's Climate Vulnerability (1981-2023)
Author: Mohammad Rabbani
Date: February 2026
Tools: Python, Pandas, Matplotlib, Seaborn, NumPy, SciPy
This project analyzes 43 years of climate data to quantify Pakistan's climate vulnerability. Despite contributing less than 1% of global greenhouse gas emissions, Pakistan ranks among the world's most climate-vulnerable nations. This analysis uses rigorous statistical methods to document temperature trends, extreme weather patterns, and environmental degradation.
Dataset:
- 1,200 total records across 10 countries
- 114 observations for Pakistan (1981-2023)
- 19 climate variables including temperature, rainfall, floods, CO2, forest cover, etc.
- Temperature increasing at 0.021°C per decade (statistically significant, p < 0.05)
- Total temperature increase since 1981: 1.17°C
- Hottest temperature ever recorded: 41.57°C (1994)
- Correlation coefficient: r=0.187, p=0.048 (statistically significant)
- 60% increase in flood events since 2000
- Pre-2000 average: 6.4 floods/year
- Post-2000 average: 10.2 floods/year
- 2022 floods were among Pakistan's worst climate disasters (dataset confirms extreme rainfall)
- Average annual rainfall: 1,030 mm
- Standard deviation: 364 mm (35% variability coefficient)
- Range: 327 mm (driest year) to 1,983 mm (wettest year)
- 6× difference between extremes indicates high climate unpredictability
- Average heatwave days: 31.4 days per year
- Heatwave frequency increasing: +2.1 days per decade
- Post-2015 average: 37.8 days (20% higher than pre-2015)
- Maximum heatwave days: 56 days in 2023
- 40% forest cover loss since 1980s
- 1980s average forest cover: 51.2%
- 2020s average forest cover: 30.8%
- Deforestation rate: 2.1% annually
- Loss of natural flood mitigation capacity
- Pakistan ranks #3 out of 10 countries in climate risk index
- Average climate risk index: 48.3 (higher = more vulnerable)
- Risk increasing despite low emissions contribution
- Loaded 1,200-row dataset, filtered 114 Pakistan observations
- Checked for missing values, duplicates, data quality issues
- Verified data integrity across all 19 variables
- No missing values found in Pakistan subset
- Trend Analysis: Linear regression for temperature, heatwaves, floods
- Correlation Analysis: Pearson correlation for variable relationships
- Significance Testing: p-value < 0.05 threshold for statistical significance
- Decadal Comparison: Grouped by decade to identify temporal patterns
- Comparative Analysis: Ranked Pakistan against 9 other countries
- Comprehensive Dashboard: 9-panel analysis of all key variables
- Global Comparison: Pakistan vs other countries (climate risk, floods, temperature, forest cover)
- Decadal Analysis: Trends across 1980s, 1990s, 2000s, 2010s, 2020s
- Infographic: Key findings summary for non-technical audiences
Languages & Libraries:
- Python 3.x
- Pandas (data manipulation)
- NumPy (numerical computing)
- Matplotlib (visualization)
- Seaborn (statistical visualization)
- SciPy (statistical testing)
Analysis Techniques:
- Linear regression (trend analysis)
- Pearson correlation (relationship testing)
- Hypothesis testing (statistical significance)
- Descriptive statistics (mean, std dev, min, max)
- Time series analysis
- Comparative analysis
- Temperature Crisis: Pakistan warming at alarming rate despite minimal emissions contribution
- Water Extremes: Alternating droughts and catastrophic floods require integrated water management
- Deforestation: Urgent need for reforestation to restore natural flood buffers
- Heatwaves: Public health infrastructure must prepare for longer, more frequent heat events
- Climate Justice: International support needed for adaptation and resilience building