Rail Infrastructure: A Systematic Modeling Framework to Quantify Customer Avoided Emissions (CAE) in Projects – Empirical Validation and Sensitivity Analysis

Sustainability Analytics and Modeling, vol. 6, pp. 100068

Abstract

ABSTRACT This study introduces a systematic modeling framework for quantifying Customer Avoided Emissions (CAE) in rail infrastructure projects, addressing a critical gap in sustainability assessment related to indirect greenhouse gas (GHG) reductions. CAE denotes the emissions that rail infrastructure customers—typically railway operators—avoid through the deployment of infrastructure solutions that enable modal shift, electrification, energy efficiency improvements, and capacity enhancements. By formalizing these mechanisms within a unified analytical structure, the framework enhances the transparency, comparability, and robustness of climate-impact evaluations in the rail sector. Methodological Contribution: The framework integrates top-down, bottom-up, and hybrid calculation approaches, structured using the MECE (Mutually Exclusive, Collectively Exhaustive) principle to ensure robustness across varying data availability scenarios. Four key CAE levers are systematically quantified: (1) electrification, (2) modal shift, (3) energy efficiency enhancements, and (4) capacity increase without new track construction. Empirical Validation: The proposed framework is validated through a case study of the São Paulo Metro Line 16–Violeta (Brazil), demonstrating approximately 421,000 tCO₂eq (carbon dioxide equivalent, a standardized metric for aggregating greenhouse gases) of avoided emissions over a 30-year period, with a sensitivity-based range of roughly 398,000 to 452,000 tCO₂eq, primarily driven by uncertainty in bus occupancy assumptions. The analyzed system configuration includes Communications-Based Train Control (CBTC), a digital train control technology that reduces headways, increases line capacity, and enables more energy-efficient rail operations, thereby enhancing the achievable emission reductions (Schnieder, 2024: [32]). Sensitivity Analysis: A cross-country comparison across Brazil, Germany, India, the United States, and China — chosen for their diverse electricity mixes and their strategic relevance due to extensive current or potential rail-infrastructure development — shows that modal shift accounts for over 99% of CAE in this case study. Differences in grid emission factors (0.25–0.81 t CO₂/MWh) lead to only ±0.03% variation in CAE, whereas bus-occupancy assumptions (30–40 passengers) result in a spread of 25%, making this the most influential parameter for this project. These results are unique to this project; in other projects, changes in transportation modes such as cars, trucks, or aviation may be shown to significantly affect the total avoided emissions. Alignment with Standards: The framework explicitly aligns with GHG Protocol Scope 3 Category 11, ISO 14064-1:2018, and UNFCCC CDM methodologies (ACM0016, TOOL18), addressing additivity, attribution boundaries, and counterfactual without-case construction. Practical Impact: This replicable framework supports data-driven decarbonization strategies, informs carbon pricing mechanisms, and enables project-specific CAE quantification for sustainable transport investments, moving beyond generalized emission factors to robust, context-specific analytics.

Authors 4

1. Annika Schmidt corresponding

   Technische Universität Braunschweig

   Affiliation as printed

   TU Braunschweig University

2. Fardad Aala Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Nikhil Dev

   Leibniz University Hannover

   Affiliation as printed

   Leibniz University Hannover

4. Lars Schnieder

   Technische Universität Braunschweig

   Affiliation as printed

   TU Braunschweig University