Ecological Footprint of Road Congestion in Kolkata City: An Integrated Traffic, Carbon Emission and Spatial Assessment Framework

Technical Summary: Ecological Footprint of Road Congestion in Kolkata City

Problem Statement
Road congestion imposes significant environmental costs by reducing vehicle speeds, prolonging idling, and inducing stop-and-go driving, which collectively increase fuel consumption and greenhouse gas emissions. While existing literature addresses transport emissions, air quality, and congestion separately, there is a lack of integrated frameworks that quantify the specific ecological burden attributable to congestion at the corridor level. Furthermore, standard carbon footprint assessments often fail to distinguish between emissions generated under free-flow conditions and those resulting specifically from congestion-induced inefficiencies. This study addresses the gap in connecting observed congestion intensity with vehicle-kilometres travelled (VKT), fuel consumption, CO₂ emissions, and their conversion into an equivalent biologically productive area (ecological footprint).

Methodology
The paper proposes the Ecological Footprint of Road Congestion (EFRC) framework, an integrated approach linking traffic dynamics to ecological accounting. The study focuses on eight major traffic corridors in Kolkata, India.

• Data Source: The analysis utilizes a synthetic transport and road-structure dataset (N = 400 observations) designed for methodological demonstration. It is explicitly noted that these are not field measurements but a structured dataset to test the workflow.
• Conceptual Framework: The framework progresses from traffic volume and classification to operating characteristics (speed, queue length, idling) to derive a congestion intensity measure. This is translated into excess fuel consumption (separating moving and idling components), converted to CO₂ emissions, and finally expressed as a carbon ecological footprint in global hectares (gha).
• Indicators: Key metrics include the Congestion Index (CI), Delay Index, VKT, and a composite Ecological Footprint of Road Congestion Index (EFRCI). The EFRCI aggregates normalized indices for congestion, fuel, CO₂, idling, and VKT.
• Statistical and Spatial Analysis: The study employs descriptive statistics, Pearson and Spearman correlations, multiple regression with variance-inflation diagnostics (VIF), Principal Component Analysis (PCA), one-way ANOVA across five Kolkata zones, and spatial analysis using Moran's I and GIS-based hotspot mapping (Getis-Ord Gi*, Kernel Density, IDW).

Key Results
The results, derived from the synthetic dataset, demonstrate the workflow and statistical relationships within the framework:

• Correlations: The composite EFRCI showed very strong positive correlations with fuel consumption (r = 0.960), CO₂ emissions (r = 0.960), and VKT (r = 0.877). Conversely, it correlated moderately with the congestion index (r = 0.384) and average speed (r = −0.383).
• Regression Analysis: A multiple regression model explained 94.2% of the variance in the ecological footprint (Adjusted R² = 0.941). The only statistically significant predictors were VKT and heavy-vehicle share. The congestion index and average speed were found to be perfectly collinear (r = −1.000), rendering them interchangeable in the model and preventing separate interpretation of their coefficients.
• Principal Component Analysis (PCA): Two principal components accounted for 86.17% of the total variance. PC1 represented a "scale" dimension (loading on EFRCI, CO₂, fuel, and VKT), while PC2 represented a "congestion vs. speed" dynamic.
• Spatial and Zonal Differences:
  • Zonal Variation: Mean ecological footprints differed significantly among Kolkata zones (F = 7.4658, p < 0.001). South-West Kolkata had the highest mean footprint (2.734 gha), while East Kolkata had the lowest (2.006 gha).
  • Corridor Performance: MG Road–Central Avenue recorded the highest EFRCI (1.68) due to low speeds and high idling, whereas EM Bypass–Science City had the lowest (1.12) despite high traffic volumes, due to higher speeds and lower idling shares.
  • Spatial Clustering: Global spatial autocorrelation (Moran's I = −0.0964) was not significant, indicating no global clustering of high-footprint corridors at the node level, though GIS mapping highlighted local concentrations in the Central Business District.

Key Contributions

1. Framework Development: The paper introduces the EFRC framework, which uniquely integrates congestion intensity, vehicle-specific fuel consumption (moving vs. idling), and carbon-uptake requirements into a single corridor-level metric expressed in global hectares.
2. Differentiation of Congestion Burden: It explicitly separates the ecological burden of congestion from total transport emissions, addressing the gap where congestion-specific impacts are often masked in city-wide averages.
3. Methodological Demonstration: The study provides a complete analytical workflow, from data synthesis to GIS-based spatial risk mapping, demonstrating how to operationalize ecological footprint analysis for specific road corridors.
4. Statistical Insights: The analysis highlights the mathematical dependency between congestion indices and speed, offering methodological guidance on variable selection in regression models to avoid multicollinearity.

Significance and Claims
The paper claims that the EFRC framework offers a more nuanced understanding of urban sustainability than traditional indicators like average speed or delay alone. By converting carbon emissions into biologically productive land area, the framework allows for the comparison of different corridors on a unified ecological scale.

The author emphasizes that the numerical results are demonstrative only, derived from synthetic data to validate the workflow. The paper asserts that the framework is designed for application with observed traffic counts, GPS speed data, and validated emission factors. It concludes that road congestion in Kolkata should be viewed not merely as a transportation inefficiency but as an environmental-resource problem, where reduced speeds and increased idling directly increase the ecological demand on the city's ecosystems. Future application of the framework requires field validation to confirm the statistical relationships and spatial patterns identified in this methodological demonstration.