A consequential framework to quantify the climate impacts of passenger travel

Abstract

Transportation analyses rely almost exclusively on attributional carbon accounting methods, which can distort the ‘carbon footprint’ of passenger travel and estimated effects of mitigation strategies. This commentary aims to facilitate a transition to consequential carbon accounting in transportation, a sector that contributes a large and growing share of global greenhouse gas emissions. A conceptual framework for consequential accounting of the climate impacts from passenger travel is proposed, which requires characterization of a counterfactual. An analytical framework is then developed to apply the consequential framework. Finally, research directions are described to advance consequential carbon accounting methods in transportation analysis.

Keywords

Greenhouse gases emissions transportation life cycle assessment consequential accounting carbon footprint

Introduction

Passenger transportation is a primary contributor to global greenhouse gas (GHG) emissions, and contributes an increasing share of total GHG emissions despite decades of efforts to improve fuel efficiency and encourage travel mode shift away from automobiles (Hodges and Potter, 2010; Intergovernmental Panel on Climate Change (IPCC), 2023; Teter and Voswinkel, 2023). In addition to technical, institutional, and behavioural barriers, a contributing factor to our inability to reach emission reduction targets may be the current GHG accounting methods used in transportation, which can distort the climate impacts of travel and mitigation strategies.

To illustrate, consider an engineer evaluating her commute travel options. She could drive her car to work (alone), which typically emits 120 g of CO₂ per km during urban travel. Alternatively, she could take the city bus, which emits 600 g of CO₂ per km, and typically has five passengers on it. In both cases, the average GHG emission rate per passenger kilometer travelled (PKT) is 120 g CO₂. But do these two travel options have the same climate impact? Intuitively not: taking the bus has less of an impact, because it is already operating on that route, whereas the car would sit idle if she did not drive it.

The average-rate comparison in this example is a form of ‘attributional’ carbon accounting, and the dominant approach in transportation analyses. An alternative approach that aims to represent the impacts of travel decisions is called ‘consequential’ carbon accounting. With a few exceptions described below, consequential accounting is nearly absent from transportation analyses, possibly due to a lack of frameworks and tools for applying it to travel.

This commentary aims to facilitate the use of consequential carbon accounting in transportation analysis. A conceptual framework for consequential accounting of the climate impacts from passenger transportation is proposed, and then an analytical approach to apply the consequential framework to quantify the ‘carbon footprint’ of passenger travel is presented. Application of the framework to real travel data is left for future work.

Carbon accounting

Attributional and consequential frameworks

Carbon accounting is the process of quantifying climate impacts, which is important for planning, design, and policy development. Carbon accounting is also visible to the public and discussed in the media, usually in the form of carbon footprints or offsets. A large and robust body of life cycle assessment (LCA) literature has developed carbon accounting frameworks and tools (Finnveden et al., 2009; Zamagni et al., 2012), although outside of the LCA academic literature, accounting frameworks are often only implicit. A key distinction in LCA methods is between attributional and consequential frameworks (Brander and Ascui, 2015; Finnveden et al., 2009; Weidema et al., 2018).

In attributional accounting, total system emissions are attributed to users of a system, typically proportional to their use (e.g. assigning total bus emissions equally to all passengers). Each user’s attributed emissions should be mutually exclusive and exhaustive to the system total. In contrast, consequential accounting attributes changes in total system emissions to the actions of agents according to the consequences of those actions. Consequential accounting can be implemented using marginal emission rates, which reflect consequential relationships, in contrast to the average emission rates that are used in attributional accounting (Beltrami et al., 2020; Bigazzi, 2019; Gagnon and Cole, 2022; Hawkes, 2014; Khan, 2019).

Past studies in non-transportation sectors have shown substantial differences in the results of consequential versus attributional analyses, which tend to be larger when there are substantial fixed (vs. variable with use) emissions-generating system components, when energy consumption rates per unit of use are inconsistent, in infrastructure systems with non-continuous supply functions (i.e. ‘lumpy’ assets), and in systems with heterogeneous, finite-capacity technologies (Corradi et al., 2021; Nordenstam, 2021; Schaubroeck et al., 2020; Thomassen et al., 2008; Weidema, 2017). Scholars have argued that consequential accounting is more appropriate for policy analysis and to inform decision-making, but that it is more challenging to implement because it requires understanding of system dynamics, agency, and causality, among other reasons (Brander and Ascui, 2015; Brander et al., 2019; Corradi et al., 2021; Curran, 2013; Finnveden et al., 2009; Russell, 2018; Schaubroeck et al., 2020; Weidema et al., 2018). Some analyses may seem inventory-based, such as carbon footprints, but are still interpreted as causal and used to inform decision-making, and so should be based on consequential accounting. For example, The Nature Conservancy’s carbon footprint calculator tool informs users that ‘A carbon footprint is the total amount of greenhouse gases…that are generated by our actions’ (emphasis added) (‘What is your carbon footprint?’ 2024).

Carbon accounting for passenger transportation

Analyses of the climate impacts of passenger transportation systems and policies almost always use average-rate methods that imply an attributional accounting framework by assigning each vehicle occupant a proportional share of vehicle-generated emissions. Some studies have used consequential accounting to quantify upstream marginal emission rates for provisioning transportation fuels including biodiesel (Reinhard and Zah, 2009) and electricity (Graff Zivin et al., 2014; Tu et al., 2020), and to account for the vehicle and infrastructure life cycle impacts of travel mode shifts (De Bortoli and Christoforou, 2020). However, these studies do not extend the consequential framing to use phase (vehicle operating) emissions.

Other past research used a consequential accounting approach to develop marginal emission factors for vehicle operation that account for varying vehicle occupancy (Bigazzi, 2019). That work demonstrated that average-rate (attributional) accounting systematically inflates the GHG emissions from vehicle operation caused by public transit use, especially in smaller cities and low-ridership systems (Bigazzi, 2020). This commentary expands that analysis by developing a broader conceptual framework for consequential life cycle carbon accounting in transportation, aiming to bring the various dynamic and demand-responsive elements of transportation systems into a comprehensive consequential accounting framework.

Conceptual framework for consequential carbon accounting for passenger transportation

Consequential accounting seeks to quantify the marginal climate forcing effects that are directly caused by, or in a clear causal chain from, the actions of an agent or the impact of a policy (Schaubroeck et al., 2020). Hence, quantifying marginal impacts requires knowledge or assumption of causality and an (unobservable) counterfactual scenario in which the action is not taken or the policy not implemented. Prospective transportation policy and planning analyses often assume an explicit ‘business as usual’ counterfactual scenario, but that is not available for observed travel without a specific counterfactual. The rest of this commentary focuses on GHG accounting for passenger travel with no explicit counterfactual.

Defining a general counterfactual for passenger travel is challenging for several reasons. Firstly, transport systems are unevenly demand-responsive: transport services respond to demand or use, but not always proportionally or consistently (due to ‘lumpy’ infrastructure provision, fixed asset and capacity constraints, minimum service requirements, and other factors). Secondly, travel is a derived demand: not travelling may mean disengagement or substitution of an activity, with additional carbon impacts. Thirdly, time is non-fungible: any net change in travel time creates some other change in time use, with additional carbon impacts.

Building on the consequential LCA literature cited above, three key principles for determining the consequences of an action are agency, causality, and utility. Agency: consequences require agency on the part of the actor, and actors can be attributed partial impacts of actions for which they have shared or partial agency. Causality: consequences follow the primary causal direction between interrelated factors, which can include direct effects (e.g. emissions from fossil fuel combustion) and indirect effects mediated by other agents (e.g. emissions during the processes of fuel or infrastructure provision). Utility: the scope of consequences should be faithful to the analysis framework they are applied in, and interface with broader GHG accounting protocols in which travel is an upstream or indirect effect.

For personal travel, travellers (or their households) are generally assumed to have agency in their own trip-making, due to agency in their activity decisions. In contrast, travel for work (not commuting) is a consequence of business activities, and so attributable to decisions of the business. The most salient consequence of passenger travel is vehicle travel, which abates to some extent in the counterfactual, depending on the causal impact of passenger travel demand on vehicle operations. Drawing a wide scope, other potential consequences of passenger travel include changes in other household travel, household activities, vehicle ownership, home and work location and type, actions of other transport system users, transport system services and infrastructure, and other non-transportation systems (e.g. fuel, land use).

A counterfactual for passenger travel in which other household travel and activities are fixed provides the most feasible framing for consequential carbon accounting. Compensating changes in other travel or activities is a more comprehensive behavioural response, but highly uncertain and contextual, and therefore less generalizable. The telecommuting literature shows that even in that specific situation, the array of indirect travel and activity shifts in a household from eliminating commutes is complex and uncertain (Kim et al., 2015; Lachapelle et al., 2018). Fixing other travel allows travel shifts to be modelled as the combined marginal effects of eliminating some travel and adding other travel. Fixing activities also allows for integration with broader GHG protocols where travel is treated as consequential to activities (Intergovernmental Panel on Climate Change IPCC, 2023).

Regarding other changes, vehicle purchases (and associated life cycle emissions) are to a large extent a consequence of intended and past usage, and so less frequent or less likely in a counterfactual with less private vehicle travel, as are vehicle maintenance activities and their associated emissions. Changes in transportation system elements that are responsive to travel demand volume should also be altered in the counterfactual, including operations (e.g. traffic congestion, dwell times), services (e.g. frequency, routes), and infrastructure (e.g. guideway construction and maintenance). In contrast, travel is primarily consequential of home and work location decisions (not vice versa), and so those factors would be unchanged in the counterfactual. Finally, although a given trip may influence the travel behaviour of others, individuals are generally assumed to have full agency over their own travel, unless they are explicitly on an escort trip.

In summary, to quantify the consequential GHG impacts of (non-work) passenger travel, in the proposed counterfactual the travel does not occur, leading to (non-proportional) changes in vehicle activity and transportation system operations. Other factors are unchanged in the counterfactual, including activities, housing, and other trips by the traveller or others. This counterfactual is useful for accounting and integration with both travel analysis and GHG protocols, but it is not realistic because it does not explicitly address the potential GHG impacts of alternative travel or time use during the avoided travel. As a few examples, the counterfactual does not include the full effects of telecommuting (which can increase non-commute trips) or ordering delivery (which shifts vehicle travel to the freight sector). More specific analysis to address topics such as these must create an explicit counterfactual, but can still use the conceptual framework by aggregating the consequential impacts of each eliminated or added trip.

Analytical framework for consequential carbon accounting for passenger transportation

We next address how to implement this conceptual framework in a quantitative analysis. Consequential accounting seeks to quantify the change in total climate forcing emissions $E$ due to the actions of an agent $α$ . Let $E$ be composed of the aggregate effects from $s ϵ S$ discrete source types or emission components, $E = \sum_{s} E_{s}$ , with three major emissions components in the transportation sector, $S = {F, V, G}$ , for the fuel, vehicle, and guideway (infrastructure) life cycles.

The analytical framework uses vehicle activity (represented as vehicle kilometres travelled or VKT) as the causal linkage between passenger travel and emissions because motor vehicle operation is the key mechanism in transportation-related climate forcing due to GHG emissions from fuelling motive energy. In addition, there is a large body of knowledge on VKT relationships with both transportation system operations and upstream emissions sources. It is useful to decompose vehicle activity into service $(s)$ and deadheading $(d)$ components, as $V K T = \sum_{t} V K T_{t}$ where $t \in {s, d}$ . Deadheading is vehicle activity without passengers (applicable to ride-hail, public transit, or fully automated private vehicles). If $E_{s}$ are differentiable functions of $α$ and $V K T_{t}$ , then we can represent the marginal effect of $α$ on $E$ as $\frac{d E}{d α} = \sum_{s} \frac{d E_{s}}{d α} = \sum_{s} \frac{d E_{s}}{d V K T} \frac{d V K T}{d α} = \sum_{s} \sum_{t} \frac{d E_{s}}{d V K T_{t}} \frac{d V K T_{t}}{d α}$ . We next examine the marginal VKT consequential to the agent’s travel $(\frac{d V K T_{t}}{d α})$ , and then the marginal GHG emissions consequential to vehicle activity $(\frac{d E_{s}}{d V K T_{t}})$ .

Marginal VKT consequential to passenger travel

Treating the action $α$ as a passenger trip, it is useful to break the $\frac{d V K T_{t}}{d α}$ relationship into two parts: the passenger activity generated from the trip $(\frac{d P K T}{d α})$ and the effect of passenger demand on each type of vehicle activity $(\frac{d V K T_{t}}{d P K T})$ . The generated PKT can be expressed $\frac{d P K T}{d α} = δ p l$ , where $δ$ is the traveller’s share of agency for the trip production, $p$ is the number of co-travellers, and $l$ is the trip length. The agency δ could be 0 for an escorter and 1 for an escortee, for example. If agency is unknown, it can be apportioned equally among co-travellers as $δ = \frac{1}{p}$ , resulting in $\frac{d P K T}{d α} = l$ (equivalent to individual passenger autonomy).

The marginal effect of passenger demand on vehicle activity varies by type. For service VKT, $\frac{d V K T_{s}}{d P K T}$ ranges from 0 to $\frac{1}{θ}$ , where $θ = \frac{PKT}{{VKT}_{s}}$ is vehicle occupancy. The elasticity of in-service VKT to PKT $(ε_{V_{s}}^{P} = \frac{PKT}{{VKT}_{s}} \frac{d {VKT}_{s}}{d PKT})$ can be used to represent the demand-responsiveness of the service VKT as $\frac{d {VKT}_{s}}{d PKT} = ε_{V_{s}}^{P} \frac{1}{θ}$ . If some portion of marginal passenger demand is accommodated through increased vehicle occupancy rather than increased VKT_s, $ε_{V_{s}}^{P} < 1$ and $\frac{d {VKT}_{s}}{d PKT} < \frac{{VKT}_{s}}{PKT}$ . For deadheading VKT, $\frac{d {VKT}_{d}}{d PKT}$ represents the proportional deadheading created by marginal passenger travel demand. The values of $\frac{d V K T_{t}}{d P K T}$ can be inferred from longitudinal system operating data reporting both $P K T$ and $V K T_{t}$ (Bigazzi, 2020). The value applied should be service-specific, to the extent that the traveller has agency in selecting the service used, but aggregate with respect to system factors over which the traveller has no agency.

Marginal GHG emissions consequential to vehicle activity

The marginal GHG emissions consequential to vehicle activity $\frac{d E_{s}}{d V K T_{t}}$ should be based on consequential LCA wherever possible, although attributional LCA may be required given the limitations of the literature. Marginal fuel life cycle emissions $\frac{d E_{F}}{d V K T_{t}}$ for a given fuel type are mediated by fuel volume: $\frac{d E_{F}}{d V o l_{F}} \frac{d V o l_{F}}{d V K T_{t}}$ , where $\frac{d V o l_{F}}{d V K T_{t}}$ is the vehicle’s fuel consumption rate and $\frac{d E_{F}}{d V o l_{F}}$ is a vehicle-independent fuel property, combining consumption (combustion) and upstream (production) contributions. A vehicle’s fuel consumption rate can vary with factors such as traffic conditions, driver behaviour, weather, and road grade. In-service fuel consumption is also affected by vehicle occupancy (and so indirectly passenger demand) for two reasons: a heavier vehicle requires greater energy to overcome inertia and losses, and higher passenger loads can lead to more frequent stops and longer dwell times for transit vehicles. Based on the development in Bigazzi (2019), the marginal effect of vehicle occupancy $θ$ on fuel consumption rate during service $(f_{s} = \frac{V o l_{F}}{V K T_{s}})$ can be accounted for as $\frac{d {Vol}_{F}}{d {VKT}_{s}} = f_{s} (1 - ε_{f_{s}}^{θ} + \frac{ε_{f_{s}}^{θ}}{ε_{V_{s}}^{P}})$ , where $ε_{f_{s}}^{θ}$ is the elasticity of in-service fuel consumption rate to vehicle occupancy $(ε_{f_{s}}^{θ} = \frac{θ}{f_{s}} \frac{d f_{s}}{d θ})$ and $ε_{V_{s}}^{P}$ is defined above. Deadheading fuel consumption rate can be assumed independent of passenger demand or $θ$ ; i.e., $\frac{d {Vol}_{F}}{d {VKT}_{d}} = \frac{{Vol}_{F}}{{VKT}_{d}}$ .

Vehicle life cycle emissions are only partially determined by, and co-causal with, distance-based vehicle usage. A vehicle’s useful life and maintenance needs are also determined by non-use factors such as time, garaging, and climate, and vehicle ownership is also motivated by non-use values such as option value and existence value. The total vehicle life cycle emissions can be decomposed into purchase (including scrappage) and maintenance (including consumables such as tyres and oil) components over the lifetime of the vehicle as $E_{V} = E_{V_{p}} + E_{V_{m}}$ , and the marginal effect of VKT quantified as $\frac{d E_{V}}{d {VKT}_{t}} = μ_{V_{p}} \frac{E_{V_{p}}}{L_{V}} + μ_{V_{m}} \frac{E_{V_{m}}}{L_{V}}$ , where $L_{V}$ is the vehicle lifetime in VKT and $μ_{V_{p}}$ and $μ_{V_{m}}$ are the proportions of purchase decisions and maintenance activities, respectively, that are consequential to distance-based usage (independent of VKT type).

Marginal guideway life cycle emissions $\frac{d E_{G}}{d V K T_{t}}$ are again only partially determined by, and co-causal with, vehicle usage. Transportation infrastructure provision is not highly demand-responsive, due to long life cycles and the indivisible and networked nature of transportation facilities. In addition, guideway lifetime and maintenance are determined by a combination of vehicle usage and non-use factors such as time, weather, and access requirements. As above, the total guideway life cycle emissions can be decomposed into construction (including end-of-life) and maintenance components as $E_{G} = E_{G_{c}} + E_{G_{m}}$ , and the marginal effect of VKT quantified as $\frac{d E_{G}}{d V K T_{t}} = μ_{G_{c}} \frac{E_{G_{c}}}{L_{G}} + μ_{G_{m}} \frac{E_{G_{m}}}{L_{G}}$ , where $L_{G}$ is the guideway lifetime in VKT and $μ_{G_{c}}$ and $μ_{G_{m}}$ are the proportions of guideway provision decisions and maintenance activities, respectively, that are consequential to vehicle usage (independent of VKT type). The calculation is sensitive to vehicle type if using a $L_{G}$ value specific to the relevant vehicle (reflecting the greater wear caused by heavy-duty versus light-duty vehicles, for example).

Conclusions

This commentary aims to facilitate analysis that more realistically quantifies the climate impacts of passenger travel by developing conceptual and analytical frameworks for consequential carbon accounting. Application of the framework is left for future work, and requires quantification of the causal relationships between passenger and vehicle activity, between vehicle occupancy and fuel consumption rate, between vehicle usage and purchases, and between transportation infrastructure provision and vehicle activity. These relationships are neglected in an attributional framework and so differentiate consequential accounting.

Some key research directions for the development of consequential carbon accounting methods in transportation analysis follow.

• More could be done to characterize appropriate counterfactuals for different analysis aims and scopes. Further work is also needed to address agency in trip-making among members of a household or co-travellers. Potential reverse causality for distal impacts such as guideway construction also warrants further investigation.

• Empirical research is needed to quantify the causal relationships described above in different contexts, particularly between passenger and vehicle travel for transit services, between vehicle usage and purchases, and between automobile travel and guideway construction.

• The marginal impact of vehicle travel on total emissions through traffic congestion could be added to the framework to account for the additional emissions from all on-road vehicles due to the incremental congestion caused by the marginal vehicle, following the analysis method in Bigazzi and Figliozzi (2013).

• Finally, outside of the transportation field, more consequential LCA is needed to quantify the upstream marginal impacts of vehicle production and maintenance, fuel provision, and guideway provision and maintenance.

A transition to consequential carbon accounting methods is overdue in transportation, a sector with high complexity and inertia that contributes a large and growing share of total GHG emissions. Consequential accounting is the most appropriate approach for analyses that aim to inform policy and decision-making. Consequential accounting is both conceptually and computationally more challenging than the attributional, average-rate methods currently used in transportation analyses. Hopefully, this commentary helps to mitigate those challenges with clearer conceptual and analytical framing, and spurs further research to provide the quantitative information required for consequential carbon accounting of passenger transportation.

Footnotes

Acknowledgements

The author wishes to acknowledge the varied contributions to the development of these ideas from colleagues, students, and reviewers.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Alexander Bigazzi

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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