Causal Insights — Systemic Map
5 significant causal relationships · PC algorithm + Bayesian network · FY 2022–2024 operational data
Operational Causal Network
Negative causal effect Positive causal effect Weak
Hover over edges to see causal coefficients (β). Arrow direction indicates causal flow. Thickness indicates effect strength.
Significant Causal Findings
Strong causal linkβ = −0.71
Fleet Deployment → Scope 1 Emissions
Each 1% increase in electric traction share reduces Scope 1 emissions by ~0.71%. The single most powerful ESG lever available to FNM management.
Recommended action
Accelerate Coradia Stream delivery to retire remaining 6 diesel sets in Q1 2025.
Projected impact
−8,200 to −9,400 tCO₂e projected saving
Strong causal linkβ = +0.58
Disruption Events → Energy Spikes
Strong causal linkβ = +0.44
Maintenance Strategy → On-Time Performance
Moderate causal linkβ = −0.39
Demand Forecasting → Carbon Intensity
Moderate causal linkβ = +0.31
Dwell Time Management → Energy Recovery
Methodology:Causal relationships were discovered using the Peter-Clark (PC) constraint-based algorithm applied to 24 months of FNM operational data (FY 2022–2024). Causal directions were confirmed using Bayesian network structure learning with a BIC score. All β coefficients are standardised regression coefficients from the causal structural equation model. Statistical significance threshold: p < 0.05.