International Journal of Environment and Climate Change

This study investigates the socioeconomic drivers of urban fine particulate matter (PM₂.₅) in Ouagadougou, Burkina Faso, over the period 2000–2024 using an integrated analytical framework that combines STIRPAT modelling, ARDL cointegration analysis, Environmental Kuznets Curve (EKC) testing, logarithmic mean Divisia index (LMDI) decomposition and Tapio decoupling analysis. The objective is to identify the relative contributions of population dynamics, urbanisation, motorisation, biomass energy use, income growth and total energy consumption to long-term changes in air pollution exposure. Annual PM₂.₅ concentrations were derived from satellite-based estimates calibrated against regional retrievals, while socioeconomic variables were compiled from national statistical and international databases.

The results indicate that annual mean PM₂.₅ increased from 41.7 to 56.5 µg m⁻³ during the study period, remaining substantially above WHO guideline values. Econometric analysis reveals severe multicollinearity in the full STIRPAT specification; therefore, a parsimonious model is required. In this model, motorisation is the most robust and statistically significant driver of pollution. ARDL bounds testing supports a stable long-run relationship between PM₂.₅, motorisation and biomass energy use, with motorisation showing a long-run elasticity of approximately 0.20. EKC analysis indicates a U-shaped relationship, with the turning point lying outside the observed income range, suggesting no evidence of income-driven environmental improvement within the sample period. LMDI decomposition identifies population growth and urban expansion as the dominant contributors, partially offset by declining per-capita biomass intensity. Tapio decoupling analysis shows weak decoupling between income and emissions throughout the study period.

Overall, the findings suggest that transport-sector growth, particularly motorisation, was the most robust long-term correlate of PM₂.₅ exposure within the available dataset, with implications for targeted urban air quality management policies.