Panel Analysis of Environmental Kuznets Curve Hypothesis in South Korea

Research code for the paper "Panel Analysis of Environmental Kuznets Curve Hypothesis in South Korea", published in NHSJS (National High School Journal of Science).

Overview

This project tests the Environmental Kuznets Curve (EKC) hypothesis across South Korean provinces and regions using panel data methods. The EKC hypothesis posits an inverted-U relationship between economic growth (GDP per capita) and environmental degradation — pollution rises with early industrialization, then falls as income crosses a threshold.

Four pollutants are analyzed using regional panel data from South Korea:

Script	Pollutant
co.py	Carbon monoxide (CO)
no.py	Nitric oxide (NO)
pm10.py	Particulate matter PM10
pm25.py	Particulate matter PM2.5

Methodology

Each script runs the following pipeline for its respective pollutant:

1. Data preparation — Merges pollutant emissions, regional GDP, population density, and total population into a panel dataset indexed by region and year.
2. Log transformation — Pollutant per capita and GDP are log-transformed; GDP² is added to capture the non-linear EKC relationship.
3. Random Effects (RE) estimation — Fits a GLS random-effects panel model.
4. Fixed Effects (FE) estimation — Fits a within-estimator fixed-effects panel model.
5. Hausman test — Tests whether RE or FE is the consistent estimator; a significant result favors fixed effects.

Model:

ln(Pollutant per capita) = β₀ + β₁·ln(GDP) + β₂·ln(GDP)² + β₃·Density + ε

An inverted-U (EKC confirmed) requires β₁ > 0 and β₂ < 0.

Project Structure

.
├── co.py               # CO panel analysis
├── no.py               # NO panel analysis
├── pm10.py             # PM10 panel analysis
├── pm25.py             # PM2.5 panel analysis
├── requirements.txt    # Python dependencies
└── data/
    ├── 1gdpkor.csv         # Regional GDP per capita (South Korea)
    ├── coEmissions.csv     # CO emissions by region
    ├── noEmissions.csv     # NO emissions by region
    ├── pm10Emissions.csv   # PM10 emissions by region
    ├── pm2_5Emissions.csv  # PM2.5 emissions by region
    ├── population.csv      # Population density by region
    ├── totalPop.csv        # Total population by region
    └── ...                 # Raw/intermediate data files

Setup

# Create and activate a virtual environment (optional)
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Usage

Run each script from the project root:

python co.py    # CO emissions EKC analysis
python no.py    # NO emissions EKC analysis
python pm10.py  # PM10 emissions EKC analysis
python pm25.py  # PM2.5 emissions EKC analysis

Each script prints:

• The merged panel dataset
• Random effects model summary
• Fixed effects model summary
• Model comparison table
• Hausman test statistic, degrees of freedom, and p-value
• Coefficient and covariance matrix comparison

Dependencies

• Python 3.12+
• numpy, pandas, matplotlib, scipy
• statsmodels
• linearmodels — for PanelOLS and RandomEffects
• pycountry_convert

Citation

If referencing this code, please cite the associated paper published in NHSJS.