Girjas Law and the Legal Erasure of Sámi Sovereignty: Land Back, Swedish Environmentalism, and the Myth of Consent

Clearcutting Permits and Grazing Pressure in Sámi Reindeer Herding Regions (1995–2024) Publication Date 2025-07-16 Description This dataset and accompanying analysis code investigate the statistical relationship between forestry permits for clearcutting and grazing pressure on Sámi reindeer herding lands in Sweden. Using panel data from 1995 to 2024, we construct a Grazing Pressure variable as the ratio of clearcutting permits to available grazing land share per region. The analysis estimates a panel regression model with fixed regional effects, demonstrating that clearcutting permits are significantly and positively associated with increased grazing pressure (p < 0.001), even after controlling for time and region. This empirical result supports the claim that forestry policy exerts structural pressure on Indigenous land-use practices, especially in regions covered by the Girjas Supreme Court ruling. This upload includes: Antal.xlsx: Source dataset with regional clearcutting permit data and grazing land shares. Full Python code for panel data regression and variable construction, compatible with Google Colab. Licenses Creative Commons Attribution 4.0 International (CC BY 4.0) Copyright Copyright (C) 2025 The Authors Keywords Sámi land rights Reindeer herding Forestry policy Panel data Indigenous governance Environmental justice Girjas ruling Grazing pressure Sweden Clearcutting permits Languages English Software / Code Block (for Colab use) # Step 1: Install linearmodels !pip install linearmodels # Step 2: Import required libraries import pandas as pd from linearmodels.panel import PanelOLS import statsmodels.api as sm # Step 3: Load Excel file df = pd.read_excel('/content/Antal.xlsx') # Step 4: Melt wide-format years into long format df_long = df.melt(id_vars=['Unnamed: 0', 'GrazelandShare'], var_name='Year', value_name='Permits') # Step 5: Rename columns for clarity df_long = df_long.rename(columns={'Unnamed: 0': 'Region'}) # Step 6: Convert to numeric df_long['Permits'] = pd.to_numeric(df_long['Permits'], errors='coerce') df_long['GrazelandShare'] = pd.to_numeric(df_long['GrazelandShare'], errors='coerce') df_long['Year'] = pd.to_numeric(df_long['Year'], errors='coerce') # Step 7: Create Grazing Pressure variable df_long['GrazingPressure'] = df_long['Permits'] / df_long['GrazelandShare'] # Step 8: Drop missing values df_long = df_long.dropna(subset=['Permits', 'GrazelandShare', 'GrazingPressure']) # Step 9: Set panel index df_long = df_long.set_index(['Region', 'Year']) # Step 10: Add constant df_long['const'] = 1 # Step 11: Run panel regression with fixed effects model = PanelOLS.from_formula('GrazingPressure ~ Permits + EntityEffects', data=df_long) results = model.fit() # Step 12: Print results print(results.summary)