The International Journal of Life Cycle Assessment (2023) 28:429–461
https://doi.org/10.1007/s11367-023-02148-y
LIFE CYCLE MANAGEMENT
Integration of consumer preferences into dynamic life cycle
assessment for the sharing economy: methodology and case study
for shared mobility
Chalaka Fernando1
· Gary Buttriss2 · Hwan‑Jin Yoon3 · Vi Kie Soo1,4 · Paul Compston1 · Matthew Doolan1,5
Received: 1 December 2021 / Accepted: 8 February 2023 / Published online: 3 March 2023
© The Author(s) 2023
Abstract
Purpose The rising of the sharing economy (SE) has lowered the barrier of purchase price to accessing many different products, thus changing the consumer decision paradigm. This paper addresses the challenge of assessing the life cycle impacts of
SE systems in the context of this new consumer decision-making process. The paper proposes a methodological framework
to integrate consumer preferences into the Dynamic Life Cycle Assessment (dynamic-LCA) of SE systems.
Methods In the proposed consumer preference integrated dynamic-LCA (C-DLCA) methodological framework, system
dynamics (SD) is used to combine consumer preference and the principal method, dynamic-LCA, which follows the ISO
14040 LCA framework. Choice-based conjoint analysis (CBCA) is chosen as the stated preference tool to measure consumer
preference based on SE alternatives, attributes and attribute levels. CBCA integrates discrete choice experiments (DCE)
and conjoint analysis features. Random utility theory is selected to interpret the CBCA results by employing multinomial
logistics as the estimation procedure to derive the utilities. Derived utilities are connected in iterative modelling in the SD
and LCA. Dynamic-LCA results are determined based on dynamic process inventory and DCE outcomes and then interpreted
aligned with the SD policy scenarios.
Results and discussion The C-DLCA framework is applied to assess the GHG changes of the transition to car-based shared
mobility in roundtrips to work in the USA. Carpooling and ridesourcing are selected as the shared mobility alternatives based
on different occupancy behaviours. Powertrain system and body style are employed as the fleet technology attributes and the
latter as an endogenous variable. Dynamic-LCA results are generated considering the high battery electrical vehicle (BEV)
adoption as the policy scenario, and results are measured against a service-based functional unit, passenger-kilometre. The
model outcomes show a significant reduction in aggregated personal mobility-related dynamic-GHG emissions by transitioning to car-based shared mobility. In contrast to the use phase GHG emissions, the production phase emissions show an
increase. The results highlight the importance of integrating consumer preference and temporality in the SE environmental
assessments.
Conclusions The proposed C-DLCA framework is the first approach to combine consumer preferences, SD and LCA in a
single formulation. The structured and practical integration of conjoint analysis, SD and LCA methods added some standardisation to the dynamic-LCAs of the SE systems, and the applicability is demonstrated. The C-DLCA framework is a fundamental structure to connect consumer preferences and temporal effects in LCAs that is expandable based on research scope.
Keywords Dynamic life cycle assessment · Consumer preference · Sharing economy · System dynamics · Consumer preferences
1 Introduction
Communicated by Yi Yang.
* Chalaka Fernando
chalaka.fernando@anu.edu.au; chalakaf@gmail.com
Extended author information available on the last page of the article
Sharing economy (SE) models such as book renting and shared
mobility are becoming more prevalent in the current business
context as a solution to economic and environmental considerations (Amasawa et al. 2020; Botsman 2015; Fernando et al.
2020a; Goedkoop et al. 1999). SE models provide access to
products without owning them and feature differently to the
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linear economy model by changing from ownership to usership. The model primarily operates in the peer-to-peer market
with similar opportunities in the business-to-consumer market.
SE is defined as an economic model that shares the underutilised pool of existing assets among users for monetary or nonmonetary benefits without changing the ownership (Botsman
2015; Gobble 2017). Product service system is defined as “a
marketable set of products and services capable of jointly fulfilling a user’s need” (Goedkoop et al. 1999) and is a subset of
the SE modes. Carsharing and common taxis are examples of
product service systems that have existed for decades (Shaheen
et al. 1998). Current market models such as ridesourcing (e.g.,
Uber and Lyft), Airbnb, book renting, garden tool renting and
private parking space rental in city areas are typical collaborative consumption modes, which is another subset of the SE
model. Ertz et al. (2016) have defined collaborative consumption as “set of resource circulation systems which enable consumers to both obtain and provide, temporarily or permanently,
valuable resources or services through direct interaction with
other consumers or through a mediator”. Some reports suggest the sharing economy value will reach 325 billion USD
in 2025 (Yaraghi and Ravi 2017). Today, the majority of the
SE models are facilitated and enhanced in their connection
with internet-based platforms (Gobble 2017). These models
reduce the upfront capital investment challenges to a variable
expense through hiring/lending. This change of expenditure in
SE models allows greater access to expensive high-tech products (Ajanovic 2015).
In SE systems, products reach their maximum longevity
levels in a shorter time and provide accessibility to newer
products than in conventional linear economy systems (takemake-use-waste) due to high levels of utilisation. For example, a taxi reaches its lifetime distance (the longevity level
of a car) in a shorter period of time than a private car. The
SE characteristics of accessing the high-tech products and
changes in longevity influence the environmental impacts
and the time they occur, compared to the linear economy
system. Technology upgrades in SE system asset pools are
faster than in the traditional ownership model. This trend
is driven by the SE systems’ consumer behaviour (Hamari
et al. 2016) and the SE mode operators (Uber 2020). The
consumer gets the opportunity to access newer products in
SE systems, with better technology. They are often more
sustainably driven and cost-effective. An example is that the
composition of hybrid-electric vehicles in the Uber fleet in
the USA is six times more than the country’s average (Uber
2020). Also, the two giant ridesourcing companies, Uber and
Lyft, have pledged to achieve a 100% battery electric vehicle
(BEV) fleet by 2040 and 2030, respectively (Lyft 2020a;
Uber 2020). These changes create challenges for calculating life cycle environmental impacts using conventional and
static methods and highlight the importance of incorporating
SE specific consumer influences and dynamic changes.
The consumer preferences in SE models differ from that of
traditional linear economy systems. The upfront capital investment, longevity of the product and variable cost (maintenance
and repair cost) are commonly considered in conventional
purchase decision-making processes. However, in SE systems,
consumer decision-making considers the cost of the service
versus the cost of ownership (Weber 2015). As a result, capital
expenditure and maintenance costs are less important than
service reliability and availability (Priporas et al. 2017). SE
models, therefore, more quickly reflect the changes in consumer preferences and behaviour, with faster market adoption (Eckhardt et al. 2019; Zervas et al. 2017). The faster
fleet electrification plans of Uber and Lyft are examples of
faster market adoptions (Lyft 2020b; Uber 2020). Hence, the
SE consumer behaviour and preferences influence temporal
characteristics of SE systems’ life cycle environmental assessments compared to linear economy modes.
Integrating temporal influences into the life cycle assessment (LCA) is required to achieve more credible environmental outcomes of SE systems (Kjaer et al. 2016). Changes
such as achieving longevity, disposing of a product at an earlier point than within the conventional lifetime and accessing newer and technologically improved products require
combining temporal aspects into the LCA calculations.
Integration of these temporal changes throughout the life
cycle, such as faster electrification and the end-of-life of
BEV batteries in ridesourcing fleets, is essential to achieve a
more reliable environmental impact outcome in SE systems.
To address the challenges of the environmental impact assessments of SE systems, this paper proposes a methodological
approach that integrates consumer preferences and dynamicLCA. The consumer preference analysis component determines
the user preferences in the decision-making process and quantifies their importance. The dynamic-LCA method is proposed
to determine the temporal environmental impacts based on its
robustness in integrating temporal variables (Roux et al. 2017). In
dynamic-LCA applications, the system dynamics (SD) method is
commonly utilised (Onat et al. 2016; Stasinopoulos et al. 2012).
The SD model interconnects the consumer preference component
and the environmental dynamic-LCA. This work proposes a new
methodology that combines consumer preference and dynamicLCA to assess the impact of SE systems effectively.
The structure of this paper is as follows. Section 2 covers
a background literature study. The proposed methodological
approach follows this in Sect. 3. An illustrative case study is
selected to apply the proposed methodology, and its applications
are discussed in Sect. 4 by choosing a car-based shared mobility
case study. In Sect. 5, the dynamic-LCA results are presented
and discussed in Sect. 6. Section 7 provides the conclusions.
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2 Background
Research has shown SE systems to be an alternative way
to reduce environmental impacts (Amasawa et al. 2020;
Fernando et al. 2020a; Harris et al. 2021). Conversely, LCAbased research has also highlighted that some SE modes are
more damaging to the environment. Fernando et al. (2020b)
and the Union of Concerned Scientists (2020a, b) have found
that greenhouse gas (GHG) emissions are higher in carsharing and ridesourcing than in private cars, respectively. The
faster technology adoption also influences the SE systems,
which can be further changed based on consumer preferences. A literature review is carried out to understand the
above aspects and presented in Sects. 2.1 to 2.4.
2.1 Life cycle environmental assessments in sharing
economy systems
The LCA method was developed to assess goods and services
(ISO 2006). Hence, the method has been employed to determine the environmental impacts of service systems that engage
single or multiple products (goods) to offer SE services such
as book sharing (Amasawa et al. 2020), Mobility as a Service (MaaS) (Fernando et al. 2020b), shared laundry facilities
(Klint and Peters 2021) and clothes (Farrant et al. 2010).
The traditional product–based functional units are not
suitable for SE systems’ LCA studies. As SE is not an ownership-driven system, the functional units of the SE models
have to represent the exact functionality of the use of the
service rather than considering the entire product lifespan.
One kilogram of laundry and p.km are two applications of
service-based functional units used in the SE system, instead
of a washing cycle and use of a car that is typically measured
in vehicle kilometres (v.km) in the linear economy system,
respectively (Fernando et al. 2020a; ISO 2006; Klint and
Peters 2021). These approaches support the measure of environmental impacts only for the particular service function
and related wear and tear components. However, Goedkoop
et al. (1999) have highlighted the importance of using a wider
definition, such as the actual monthly transport activities of
two persons. Their study further elaborates on the importance
of equal levels of user satisfaction in those modes to use a
wider definition. However, later studies on shared mobility have widely used occupancy-integrated functional units
such as p.km to interpret the LCA outcomes of car-based
MaaS modes (Aamas and Andrew 2020; Amatuni et al. 2020;
California Air Resource Board, 2019; Dang et al. 2021; Ertz
et al. 2016; Fernando et al. 2020a, b; Union of Concerned
Scientists 2020b). The selection of passenger (occupancy)
and distance integrated functional units provides a practical approach to communicating and interpreting results with
fewer assumptions compared to wider definitions, such as
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transport during a month. Therefore, the selection of the
functional unit that integrates occupancy, such as p.km, is
more critical when comparing different service offerings
and comparing linear (ownership) and SE systems with different consumer preference (satisfaction) levels (Chun and
Lee 2017). It also enhances the ability to compare results,
such as the transition between car-based personal mobility
modes. However, the LCA method itself cannot incorporate
the environmental impacts of systems, such as product-service-system, influenced by user behaviour changes (Kjaer
et al. 2016).
The dynamic-LCA method integrates temporal effects that
can interpret the SE results more robustly and structurally
aligns with ISO 14040 framework. LCA is a static environmental assessment method that does not consider the temporal effects. Lueddeckens et al. (2020) have found that the
precision of LCA outcomes is challenged by not addressing
the whole range of temporal (dynamic) issues. Pinto et al.
(2019) highlight the dependency of exogenous sources on
future trends or behaviour as a threat to the LCA method.
They also emphasise maximising endogenous dynamics. The
dynamic-LCA has been utilised to overcome the above issues
by integrating dynamic and endogenous variables and their
feedback (causality) into the LCA applications (Stasinopoulos
et al. 2012). Applying dynamic-LCA has shown insights in
environmentally assessing those products with a longer life
(Changsirivathanatahathamrong et al. 2001; Collinge et al.
2013; Halog and Manik 2011; Roux et al. 2017). The method
has been used to assess different products such as mobile
phones (Yao et al. 2018), steel (Pinto et al. 2019) and SE
services such as public transport (Ercan et al. 2016).
2.2 System dynamics in sharing economy
The SD method has been used to understand the timedependent variables and their causal relationships in SE
systems. The method has also been utilised to quantify environmental impacts in different shared service models by integrating temporal feedbacks (Astegiano et al. 2019; Geum
et al. 2014; Luna et al. 2020; Stasinopoulos et al. 2021; Wang
et al. 2018; Wasserbaur et al. 2020). Esfandabadi et al. (2020)
have found four shared mobility-related system thinking,
mainly SD-based case studies, in their review. They found
that the SD can be utilised to analyse policy (He and Li 2019)
and environmental impacts (Astegiano et al. 2019) in SEbased mobility studies. Lee et al. (2012) have utilised the SD
approach to measuring triple bottom line sustainability and
developed integrated causality diagrams. The SD model also
connects causality to the system, which provides a solution to
break exogenous system dependency by combining the market (Geum et al. 2014) and consumer preferences elements.
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The incorporation of the Bass diffusion model to analyse
consumer preferences in the SE system is more applicable
compared to other approaches. Past studies have also utilised
agent-based modelling in analysing the sharing economy system behaviour (Querbes 2018). However, it was not employed
to analyse temporal (dynamic) modelling connected with
consumer preferences. Instead, the Bass diffusion model has
been employed to integrate consumer preferences into the
SD modelling (Jiang 2019; Wang et al. 2016). The model
describes the behaviour for the diffusion of innovation in
the early stages of innovation and assumes the total adoption rate in the early transition resulted from word-of-mouth
(WoM) and advertising (Bass 1969; Sterman 2000). Many SE
systems are still in their early stages of innovation. Hence,
the Bass model can be applied to SE systems based on their
early stage market status (which can also be modelled using
agent-based modelling approach) and its historical use in SE
systems. Zhang et al. (2020) adopted the Bass model in their
one-way carsharing case study and proved its applicability
in a SE system. The Bass model–derived methodological
approaches have also been applied in SE systems (Franco
2019; Wasserbaur et al. 2020). This explanation also fits with
SE models such as Airbnb, ride-hailing and jeans-sharing.
In these models, the initial adoption is based on a higher
advertising effect and later through the adoption from WoM.
2.3 Dynamic‑LCA method—combining
the temporal effect for LCA in SE
The dynamic-LCA has emerged as the most commonly used
methodology to integrate temporal effect into LCA since
1991, and there have been 165 applications by 2016 (Sohn
et al. 2020). Sohn et al. (2020) have also found the majority
of the dynamic-LCA works (44 out of 55) have followed
the ISO 14040 based LCA phases. The literature revealed
that the SD method is used as the interface to integrate the
dynamic and causal (feedback) effects into dynamic-LCA
work (Stasinopoulos 2013; Stasinopoulos et al. 2021).
The SD method is used in integrating both temporal and
causality effects into the LCAs. Two approaches that have
been used in combining SD and LCA (or GHG/sustainability
indicators) are shown in Fig. 1. Approach 1 was developed
by Halog and Manik (2011) and is frequently used. It calculates the LCA/GHG results in the conventional approach
and then feeds into an SD model to include the temporal and
causality effects. In Approach 2, the temporal and causality
effects considered SD outcomes are utilised to calculate the
life cycle inventory (LCI) (Kumar et al. 2019). The existing research has revealed that SD is an effective method for
combining temporal and causal effects in LCA, GHG and
other sustainability assessments (McAvoy et al. 2021).
There is limited research combining temporal effects into
the LCAs of SE systems. Chen and Huang (2019) concluded
the importance of establishing dynamic-LCA analysis in
product service systems. Amasawa et al. (2020) have recognised the importance of the SE asset pool lifetime changes.
However, they have not integrated the temporal or causality
effects into their LCA calculations. Only two scenario-based
case studies have been identified by the authors that utilised
the dynamic-LCA method to assess the temporal environmental impacts of SE systems. These studies include sharedownership autonomous vehicle fleets (Stasinopoulos et al.
2021) and pay-per-wash washing machine services (Sigüenza
et al. 2021). Both studies used secondary literature for consumer adoption scenarios. These works confirm the ability to
apply the dynamic-LCA method in SE systems. The findings
also suggest that research on dynamic-LCA application in SE
systems is still emerging.
The dynamic-LCA method has also been used for integrating the temporal and causal effects of the technology
transitions into the environmental assessment of SE systems
(Sigüenza et al. 2021; Stasinopoulos et al. 2021) and complies with the LCA methodology principles (Stasinopoulos
2013). Garcia et al. (2015) have applied dynamic-LCA to
determine the GHG impacts in BEV transition. The technology changes in shared products (SE asset pool), such as
the transition from internal combustion engine vehicles to
BEVs in ridesourcing fleets (Uber 2020), produce significant differences throughout the life cycle, starting from production until the end-of-life. Querini and Benetto 2015 have
highlighted the causality elements that influence the LCA
modelling in their powertrain system-based LCA work to
assess fleet electrification. However, they have not considered the temporality effects. Temporal technology changes
Fig. 1 Two main approaches
in combining SD and LCA/
GHG/sustainability indicators
(Sus, sustainability indicators).
Modified based on (McAvoy
et al. 2021)
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in SE system asset pools significantly influence production and end-of-life impacts compared to the linear system.
Shared products such as leased jeans, ridesourcing car fleets,
shared books or pooled electrical machinery would reach
their maximum product longevity in a shorter time period
than an owned private product. Hence, the product cycle and
upgrading to newer versions in SE systems are faster than
owned private products. Therefore, a dynamic-LCA-based
study is necessary to environmentally assess the changes of
the impacts caused by SE systems.
Consumer decisions are significant factors influencing
SE systems (Eckhardt et al. 2019; Xu 2020). The decisions
are driven by peer-to-peer or consumer-to-consumer interactions in temporarily accessing under-utilised physical assets
(Frenken 2017). As discussed in Sect. 2.2, according to the
Bass model, the adoption is a function of advertising and
WoM contributions. The Bass model is just a simplistic (yet
powerful) representation of adoption (Jha et al. 2008). The
consumer values can influence SE systems’ final decisionmaking (Piscicelli et al. 2015). Hence, identifying and integration of the consumer values that contribute to the model
decisions is significant for accurate modelling.
2.4 Consumer preference integration
Conjoint analysis is a frequently used stated preference technique for predicting consumer preferences for multi-attribute
alternative options of products or services. As products and
services are a bundle of attributes and conjoint determines,
the degree consumers value specific products, which leads to
a purchase behaviour intention (Green and Srinivasan 1978).
In contrast to traditional rating or ranking-based methods that
simply capture consumer attitudes, conjoint analysis measures the constrained choices from a given conjoint block by
making trade-offs. Consumer produces a rating of preferences
and places the utility. Conjoint analysis results are interpreted
using utility (or part-worth) values, defined as “attractiveness
of an alternative expressed by a vector of attributes values
reducible to a scalar” (Ben-Akiva and Lerman 1987).
Choice-based conjoint analysis (CBCA) integrates the
features of both discrete choice experiment (DCE) and
conjoint analysis (Cohen 1997). CBCA results derive utility of value from a consumer place on a product or service
attributes. Trade-offs consumers are willing to make need
to be made between attributes and attribute levels. Since
the consumer’s choice is constrained, it simulates a reallife buying decision process. CBCA, also known as brand
conjoint, allows for analysing the alternative product or
services, an essential feature in SE case studies. Generally,
SE modes have been compared against the most relevant
linear economy case, such as a washing machine, in washing
service-based SE studies (Amasawa et al. 2020; Sigüenza
et al. 2021). In some instances, different SE alternatives are
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also included by providing multiple choices to the consumer
preference experiment (Amasawa et al. 2020; König et al.
2018). Based on this study’s intentions, different technological scenarios of SE assets can also be considered attributes
in these instances. CBCA simplify aspects of alternatives
(also known as modes or brands), attributes and attribute
levels to make the consumer decision whilst compatible
with the DCE method (Ben-Akiva and Lerman 1987; Jiang
2019). Past work has integrated consumer preference and
behaviour into the LCA calculation (Krystofik et al. 2014;
Polizzi di Sorrentino et al. 2016; Querini and Benetto 2015;
Shahmohammadi et al. 2018). Polizzi di Sorrentino et al.
(2016) employed a similar approach to the stated preference
technique to quantify LCA findings based on consumer preferences. Folkvord et al. (2020) and Shahmohammadi et al.
(2018) have highlighted the importance of the attributes in
the accuracy of consumer behaviour integrated LCA studies.
Standard and mixed logit models are the most prevalent
modelling techniques in stated preference choice experiments
(Ben-Akiva and Lerman 1987; Train 2009). Standard logit/
multinomial logistics (MNL) is a commonly used discrete
choice estimation procedure in conjoint analysis results analysis (Ben-Akiva and Atherton 1977). The conjoint analysis
supported software employs the hierarchical Bayesian multinomial method estimation procedure (Conjoint.ly, n.d.).
In particular, the hierarchical Bayesian model is capable of
individual-level (preference) data analysis and is applicable
in smaller sample sizes (Lenk et al. 1996). Hence, the method
also supports determining the aggregated level preferences
based on individual preferences beyond the typical conjoint
analysis results of the importance of attributes.
There are three common approaches found in the previous
research integrating conjoint analysis and SD to simulate the
temporality, causality and influence of consumer preferences.
They are (a) integrating the most responsive attribute using a
logarithmic utility relationship (Wang et al. 2016; Wang and
Lai 2020), (b) considering all or some of the attributes as exogenous (Jiang 2019; Schmidt and Gary 2002) and (c) consider the
temporal variables using the “utility elasticity of the attribute”
(Derwisch et al. 2016; Kopainsky et al. 2012). The latter is only
reasonable when the part-worth utility curve is linear (Kubli
2020). The five-step iterative modelling process introduced
by Sterman (2000) is a commonly used approach to combine
conjoint analysis findings into the SD simulation (Schmidt and
Gary 2002; Wang et al. 2016). Jiang (2019) has combined DCE
based CBCA method and SD to model the influence of different powertrain types based on consumer preferences. Therefore,
DCE and SD combined approach can be utilised to extend the
integration of consumer preference into a more detailed analysis
level, such as alternatives or brands.
Consumer preferences in SE systems are different from
that of the traditional product economy. In a product economy,
the consumer is critical in product-based–decision-making,
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The International Journal of Life Cycle Assessment (2023) 28:429–461
considering ownership and capital investments. However,
there is a low barrier to choosing SE models by the consumer
compared to traditional product models. This convenience
increases the SE consumers’ desire to demand newer hightech products with improved service functionality. Hence, the
integration of consumer preferences and temporal changes
in the SE asset pool in environmental LCA analysis is crucial. Past studies have highlighted the importance of integrating consumer preferences into the LCA work for more
practical interpretations of the results (Bartolozzi et al. 2013;
MacLean and Lave 2003). Hicks and Theis 2014; Querini and
Benetto 2015, 2014 have used agent-based modelling technique to integrate, mainly policy-based people behaviour
inputs, with LCA modelling. Walzberg et al. 2019 have
also utilised agent-based modelling and considered temporal changes for a short period. However, no previous studies
have quantified the impact of combining measured (surveyed)
consumer preference analysis, dynamic (technology) changes
and their causal influence with an LCA study. A novel methodological approach is proposed in Sect. 3 to incorporate
consumer preferences into dynamic-LCA to assess the environmental impacts of SE systems effectively.
3 Methodological framework
This work proposes a methodology to establish consumer
preference incorporating dynamic-LCA to evaluate the
environmental impacts of services offered in SE systems.
Dynamic-LCA, which follows ISO 14040 LCA methodology, has been chosen as the key method in this proposed
methodological approach. The following approach has been
taken to develop the proposed methodology, inspired by
the dynamic-LCA applications in SE systems. It starts by
considering the SD method as the model interface in combining the outcome of DCE and LCA. Then, a structured
methodological framework is introduced by integrating
consumer preferences (measured by DCE), temporality and
LCA. Finally, a detailed model integrating SD and LCA and
DCE and SD is established. The methodological approach
is discussed in Sects. 3.1 and 3.2 and is further elaborated
in detail in Sects. 3.3 to 3.9.
Fig. 2 Methodology approach: consumer preference integrated dynamic
life cycle assessment (C-DLCA) (based on Jiang 2019; Sohn et al. 2020;
Stasinopoulos et al. 2012; Sterman 2000; Wang et al. 2016; Yao et al.
3.1 Methodology approach—system dynamics
as the interface of consumer preferences
and dynamic‑LCA
The ISO 14040 based LCA principles are followed in
building the dynamic-LCA model in this methodological
approach. The technical standard can be applied to both
products and services (ISO 2006), which enables its use for
SE systems. The proposed methodological approach defines
the LCA goal and scope as the first process and ends with
generating the life cycle impact assessment (LCIA) scenarios and interpretation (Fig. 2).
The SD method is selected to systematically and structurally combine consumer preferences with dynamic-LCA in
the proposed methodological approach. Prior studies have
employed the SD method in consumer preferences studies
(Jiang 2019) and dynamic-LCA models (see Fig. 1). The
widely used Sterman’s five-step iterative process (Sterman
2000) is selected as the SD modelling process in the proposed methodological approach shown in Fig. 2. This
iterative process provides a systematic and straightforward
approach to applying SD in a case study. It resembles a constant iterative process as the SD interface and excludes the
limitation of applying the conventional LCA and SD integration approaches, as shown in Fig. 1. The proposed methodological approach features the feedback process, non-linear
sequence steps and, notably, constant iteration. The approach
also provides a solution to combine three methodological
components: (a) consumer preferences effects measured by
the DCE method, (b) consideration of temporal and causal
feedback in SD modelling and (c) generating environmental
life cycle assessment analysis, to quantify the integration of
consumer preferences into the LCA. Therefore, the integration enables to perform a consumer preference integrated
dynamic-LCA (C-DLCA).
A hybrid combination of Approach 1 and 2 in Fig. 1 is utilised in the methodological approach. After studying the applications in previous studies (Collinge et al. 2013; Stasinopoulos
et al. 2012; Yao et al. 2018), a mixed approach is proposed to
integrate LCA before and after SD modelling. The interactions between the SD and LCA components demonstrate a data
feedback loop starting from the formulation and simulation
2018)). Legend: orange coloured—SD process steps; green coloured—
LCA phases; blue coloured—consumer preference inputs
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The International Journal of Life Cycle Assessment (2023) 28:429–461
process, LCI, LCIA, and end up in the formulation and simulation process. The process step “formulation and simulation”
plays a pivotal role in the proposed approach. Its two outcomes
are dynamic process inventory and dynamic system inventory,
which add temporal effects to the conventional LCI.
The proposed methodological approach is employed to
develop the detailed methodological framework in Sect. 3.2.
3.2 Proposed consumer preference integrated
dynamic‑LCA methodological framework of SE
systems
A methodological framework is introduced to combine consumer behaviour and dynamic-LCA based on the introduced
C-DLCA approach to assess SE systems. As highlighted
in Sect. 2.4, integrating consumer preferences is critical to
gathering information directly from stakeholders involved to
understand the temporal effect in the SE system analysis. The
C-DLCA approach utilises SD as the interface that integrates
consumer preferences and the LCA model to represent the
temporal and causal effects. This section presents a robust and
structured C-DLCA methodological framework that uses SD,
LCA and conjoint analysis methods, as presented in Fig. 3
and is consistent with the C-DLCA approach shown in Fig. 2.
There are three critical stages in the proposed C-DLCA
methodological framework resembling constant iterations.
Stage 1 (S2, (C1, C2, C3), S3) connects the DCE and the SD
models as explained in Sect. 3.1 by modifying the previous
Fig. 3 Proposed C-DLCA methodological framework of SE systems.
DPI, dynamic process inventory. (The red numbers in each process box
propose the most practical sequence of the steps; ash and black arrows
(hexagons) represent the feedback flows and data flows, respectively.
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work (Jiang 2019; Wang et al. 2016). Its application is presented in Sect. 3.4. Stage 2 is the five-step iterative SD
process model proposed by Sterman (2000). As shown in
Fig. 3, the step formulation and simulation (S3) acts as the
data and information exchange process step in the C-DLCA
methodology framework. Its application is presented in
Sect. 3.5. These steps help to understand the systems and
the mental models of the real world. The interconnections
indicated in the centre of the diagram represent the possibility of iteration occurrence. Each step starting from the
problem articulation (boundary setting) is utilised to combine the information or data flow of DCE and LCA models.
Stage 3 (L1 to S1, L2a to S3 and S3, L2, L3, S3 to S5, L4)
represents part of the beginning and the end of the structured
C-DLCA framework. It connects SD and LCA models and
is presented in Sect. 3.3.
The proposed C-DLCA methodology framework has
provided a structured combination of consumer preferences
based on primary data from consumer preference surveys
and the LCA by employing the SD method as the interface
to integrate dynamic and causality characteristics. Its robustness is checked in a case study presented in Sect. 4, and the
final dynamic-LCA results are presented in Sect. 5.
3.3 LCA and SD model design
In this section, the SD and LCA methods are integrated following the C-DLCA methodological approach and framework
Components represent in the framework: blue—DCE model and consumer preference data; yellow—system dynamic modelling interface
(the star mark resembles the iteration characteristics of the chosen SD
modelling framework (Sterman 2000)); green—LCA phases)
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established in Sects. 3.1 and 3.2. In the C-DLCA, hybrid integration of SD and LCA methods is utilised instead of the
conventional modelling approaches shown in Fig. 1. Hence, it
is a combination of Approaches 1 and 2, shown in Fig. 1, with
multiple iterations between SD and LCA models. The three
connections identified in Stage 1 in Sect. 3.3 combine SD and
LCA models, as shown in Fig. 3. They are connecting goal
and scope (L1) with problem articulation (S1), dynamic-LCI
(L2) and formulation and simulation (S3) and finally connecting LCIA and interpretation (L4) to policy scenarios (S5).
These are discussed in Sects. 3.3.2, 3.3.4 and 3.4 and 3.7,
respectively. The SD and LCA integration in the C-DLCA
framework starts with the first step of the LCA methodology,
as discussed in Sect. 3.3.1.
3.3.1 Setting up the LCA goal, scope and functional unit (L1)
The goal of the LCA is derived based on the specific research
problem and relates to a transition to SE systems. The objective setting process of the LCA follows the conventional
approach based on the identified research goal(s). The scope
of the LCA is a comprehensive collection of both the SE
assets (product) and business-as-usual (linear economy)
products utilised or impacted in a transition to SE systems.
As discussed in Sects. 2.1 and 3.1, extending the scope to
production and end-of-life phases is more significant for SE
systems. The proposed extension enables the integration of
the temporal impacts of production and end-of-life phases
of SE systems, considering typical technology upgrades and
achieving maximum product longevity in a shorter lifespan.
The scope must be decided case-by-case considering the goal,
significance of the contribution to the model and resources.
Selecting a service-based functional unit is essential in
interpreting the SE environmental impacts. Service-based
functionality units such as p.km are more suitable than
distance-based (v.km) to assess mobility servitisation modes
(Fernando et al. 2020b; Union of Concerned Scientists
2020b). As discussed in Sect. 2.1, the service-based functional unit, p.km, fits this study and is proven with the wider
application in quantitative environmental analysis in car-based
shared economy studies (Fernando et al. Under review). Typical product-based references such as per metre square, per car
and per machine neither effectively interpret the LCA results
nor do they allow comparison between two different SE alternatives. Kjaer et al. (2016) have highlighted the challenges in
defining the functional unit when conducting an LCA of a
product service system. Hence, it is essential to check the ability to compare both the SE alternatives and the business-asusual product (representing the linear economy) by employing
the selected service-based functional unit.
3.3.2 Connecting LCA goal and scope (L1) with the SD
problem articulation (S1)
The SD and LCA model integration starts with connecting
the defined goal and the scope of the LCA (L1 in Fig. 3)
with problem articulation (S1 in Fig. 3), the first of Sterman’s five steps (Sterman 2000). In this step, the problem
is defined with key dynamic variables, boundary, the time
horizon of the simulation and problem definition, including
reference modes and historical behaviour (Richardson and
Pugh III 1981; Sterman 2000). Hence, the defined goals and
scope are direct inputs into SD system problem articulation
(information flow from L1 to S1 in Fig. 3). It is crucial to
map the relationship between the defined functional unit
in the LCA and the dynamic problem definition, the reference modes. These synchronisation steps are significant in
understanding the key variables of the SD model to develop
a draft feedback structure.
3.3.3 Establishing the dynamic hypothesis (S2)
The dynamic hypothesis initiates the conceptualising of
the consumer preference experiment. Richardson and Pugh
III (1981) define it as “a statement of system structure that
appears to have the potential to generate the problem behaviour”, and Sterman (2000) refers as “a working theory of
how the potential problem arose”. Step two (S2 in Fig. 3)
depends on the articulated problem in Sect. 4.1. In the process of dynamic hypothesis generation, endogenous consequences are identified to map causal structures that are
typically presented using causal loop diagrams. Causal loop
diagrams are utilised to explain the causal relationships in
the dynamic-LCAs (Onat et al. 2016; Stasinopoulos et al.
2012). In the proposed C-DLCA method, the causal loop
diagram process is extended to integrate the LCA-based
environmental impact calculations in addition to identifying the contributing endogenous variables. This extended
step helps to understand the requirements of the consumer
preference experiment to generate the identified reference
mode(s) of the SD model by considering the LCA model
requirements. In the C-DLCA method, establishing the LCI
framework is proposed before the formulation step and is
discussed in Sect. 3.3.4.
3.3.4 Life cycle inventory considerations (L2a)
Establishing the LCI framework (L2a in Fig. 3) begins with
a qualitatively hypothesised model based on the set goal and
scope. The hypothesis process supports listing all input data
fields required to calculate the LCI. Then, these fields are
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categorised according to the two following definitions. First
is the source of data field: either within the system or exogenous based on external data sources (e.g., life cycle impact
factors). Data within the system are endogenous and depend
on the feedback from the SD system. The second classification is based on the dynamic-LCA good practises: data fields
within dynamic process inventory or dynamic inventory
analysis and dynamic system inventory (Sohn et al. 2020;
Su et al. 2021). The dynamic system inventory process is
not considered in the proposed methodology. Based on the
set scope, dynamic process inventory fields can be either
endogenous or exogenous (e.g., temporal energy production
systems—fuels used in a MaaS fleet). The selection of the
attributes and their integration in the dynamic-LCA calculations are discussed in Sects. 3.4.2 and 3.5.
Step L2, LCI calculation, is dependent on three data
inputs. The formulation and simulation step outcomes (data
flow D2 in Fig. 3) provide the SD system-generated data
inputs (Sect. 3.4). The second data input is from LCA databases whilst using the LCA software to model the system.
The third is directly from the outcomes of the consumer
preference experiment (data flow D1b in Fig. 3) and is often
used to calculate the functional unit.
3.4 Discrete choice experiment–based consumer
preference survey (C1 to C3)
The DCE model provides the primary data that decides consumer preferences for the transition to the SE system. A
market survey is designed (C1) as the first step within the
DCE model. It has two key components. They define the
sample and design the survey instrument. The selection of
the sample for the market survey is crucial in determining
the inputs to the C-DLCA framework. Hence, the LCA goal
(L1) and SD model articulation (S1) are two key factors that
define the survey sample. The survey instrument combines
the DCE model integration (C2) and other descriptive questions that are required to analyse the sample’s characteristics.
The DCE model integration and analysis are discussed in
Sects. 3.4.1 to 3.4.3.
3.4.1 Stated preference method
The conjoint analysis technique (Green and Srinivasan 1978)
is selected as the stated preference method in understanding
SE consumer preferences. It is a widely used technique to
assess the transition to different product technologies (Jiang
2019) and is also used to quantify SE systems such as shared
mobility service attributes (König et al. 2018). The conjoint
analysis technique is capable of quantifying constrained
choice modelling. Hence, it provides more meaningful
437
consumer preference results closer to the actual market.
CBCA is selected as the most suitable conjoint analysis type
for the proposed C-DLCA framework (Sect. 2.4). The selection is based on its ability to integrate the DCE model (Cohen
1997) and analyse the alternatives based on the importance
of attributes and attribute levels (Jiang 2019; Wang et al.
2016). The attributes discussed in Sect. 3.3.4 that relate to
the LCI framework have to be considered in the attributes of
the DCE model. CBCA supports constrained-choice modelling, and alternatives (also known as brands, e.g., carpooling
and ridesourcing in a shared mobility-based survey) can also
be operationalised. The survey is the primary data source of
the C-DLCA to quantify the attributes and is executed within
the chosen population.
The most influential consumer decision-making attributes
are chosen from the exploratory research. The compatibility
of the selected attributes is checked with the formulation
and simulation process (S3) before finalising the steps of
survey design (C1) and the DCE model (C2). This extra step
is introduced beyond the approaches represented by Jiang
(2019) and Wang et al. (2016). This extra iterative process
step ensures the synchronisation of the DCE results with
the SD simulation. In the C-DLCA framework, the CBCA
experiment is designed based on the dynamic hypothesis
and the data requirements of the formulation and simulation
model. The feedback flows from both problem articulation
(S1) and formulation and simulation (S3) to the DCE model
(C1-C3) in Fig. 3 are introduced, considering the nature of
the SE systems by modifying (Jiang 2019; Wang et al. 2016).
3.4.2 Random utility theory and discrete choice experiment
Random utility theory–based DCE is selected to interpret
the CBCA results. Step C3 represents this process in Fig. 3.
DCE is a robust technique that has been shown to be useful
for simulating customers’ actual market behaviour (Louviere
et al. 2010). Random utility theory is also a method for interpreting the choice behaviour of humans (Louviere et al. 2010).
As defined in Eq. (1) (Train 2009), random utility models
represent a decision-maker (n) that faces a choice among J
alternatives. The utility that decision maker n obtained from
alternative j is Unj, j = 1,2,…, J,
Unj = 𝛽n � xnj + 𝜖nj
(1)
where xnj is a vector of attributes relating to alternative j and
person n; βn is a vector of partworth utilities for the attributes that depict a person n’s tastes associated with each of the
observed variable (attribute in this case); εnj is an error term that
is an independently and identically distributed standard normal
distribution with mean 0 and standard deviation 1 (n (0, 1)).
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3.4.3 Multinomial logistic (logit) model
The MNL/logit model is utilised in the C-DLCA framework.
MNL is the most widely employed method to analyse DCEs
(Ben-Akiva and Atherton 1977; Train 2009) and is utilised in
SE consumer preference assessments to analyse the stated preference-based survey outcomes (Carroll et al. 2017; Malichová
et al. 2020). The method is also used in the CBCA to assess
the attributes at the aggregate level (Eisen-Hecht et al. 2004).
The MNL model–based logit choice probability is expressed
in Eq. (2) (Ben-Akiva and Atherton 1977; Train 2009). The
probability that individual n would choose alternative j out of
J alternatives (carpooling and ridesourcing in the case study)
is given by
�
exp𝛽 xnj
Probnj = ∑J
j=1
exp𝛽 � xnj
(2)
which is the exponential of the utility of alternative j divided
by the sum of the exponentials of utility values of all alternatives in the choice set (in this study, j represents target
SE system). SE system attractiveness (Ej) value for each
alternative is derived by calculating the probability using
the total of the (attribute) level utility values. The single
attribute value, Ej, the scalar parameter for each alternative,
is selected to represent the consumer preference attributes
in the proposed C-DLCA methodology. The integration of
Ej in modelling is discussed in Sect. 4.5.1. The calculations
are based on the conjoint analysis outcomes of the survey.
No specific statistical estimation procedure was selected to
operationalise the MNL in the proposed methodology. However, the hierarchical Bayesian model is often used in conjoint analysis-supported survey software to calculate MNL.
3.5 Formulating the simulation model (S3)
The step formulation of the simulation (S3 in Fig. 3) is
developed to cater for the identified temporal LCI inputs
and dynamic process inventory. The consideration of the
LCI framework in the C-DLCA method is an extended step
beyond a typical SD case study. This process step is planned
based on the listed variables in the LCI hypothesising process
discussed in Sect. 3.3.4. In the case study, the technological
changes (e.g., car body style) in the carpooling and ridesourcing fleets are considered to identify the inputs to calculate
dynamic process inventory. Defining the SD model structure, decision rule specification, estimation of parameters and
consistency test are the key components in this process step.
The model structure is also designed to accommodate consumer preference findings (e.g., based on the car body styles
and trip fare in the case study). Hence, the iterative process
between the formulation and simulation process step and the
consumer preference experiment is important.
The SD model step simulation and formulation (S3)
represent the connecting interface of consumer preference
survey outcomes and LCI results into the SD modelling. As
presented in Fig. 3, in the C-DLCA framework, the process
S3 enables the hybrid approach adopted in this research by
connecting and functioning as the data exchange process
to incorporate SD and LCA methods. As shown in Fig. 3,
it links data from consumer preference (D1a), LCI input
data (D2) and LCIA outcome data flow (D4) and finally
connects them all with the step, testing (S4) via the data
flow D5. Step S3 also facilitates information flow from the
LCI framework with the consumer preference study. Hence,
formulating the simulation modelling work is complex. Secondary data–based exogenous variables are also common
in SD simulations. However, the exogenous relationships
are not represented in the C-DCLA framework in Fig. 3 for
simplicity and to ensure generic applicability.
3.6 Model optimisation (S4)
The dynamic model simulation starts with the model calibration step (also known as testing; see step S4 in Fig. 3).
The calibration process supports reproducing the real-world
behaviour in the SD model to achieve the goal of the model.
Generally, the testing is performed by calibrating against the
historical values of the reference mode(s). This step is critical
for the constants, the precise values of which are difficult to
gauge since most of these parameters are intangible. Model
calibration is performed to estimate the values of these model
constants to ensure the robustness of policies, specifically
under extreme conditions and sensitivity analysis. An example
is the high BEV adoption in ridesourcing (see Sect. 1). The
calibration and optimisation step also supports assessing the
SD model structure and model parameters based on their sensitivity to uncertainties (Sterman 2000). In a dynamic-LCA,
the generated LCIA results can also be used to measure sensitivity. The sensitivity outcomes initiate the policy scenarios.
3.7 Policy scenario analysis (S5)
Step policy decision and evaluation (S5 in Fig. 3) is used to
generate dynamic-LCIA scenarios and interpretations in the
proposed C-DLCA framework. This is the last step in the
SD model, which contains scenario specifications, policy
designs and extended sensitivity analysis (data flow D7).
Based on the testing outcomes, scenario specifications and
policy designs are listed to improve the robustness of the SD
outcomes, including the LCIA results.
Two types of policy scenarios are related to SE system
dynamic-LCAs. They are based on consumer preferences
and technological changes in SE assets. The effect of the
changes in carpooling and ridesourcing modes’ fare structure is an example of a consumer behaviour–based policy
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scenario. Also, a fleet upgrade, such as introducing a BEV
fleet, can generate a technology change based on potential
policy scenarios. These policy scenarios are employed to
explore the possible responses to problem articulation (S1
in Fig. 3). There are instances where independent system
variables can also be utilised as scenarios. A change in the
renewable component in an electric grid outside the SD
scope is a typical example of an SD system-independent
(exogenous) policy scenario.
3.8 Dynamic process inventory (L2)
and dynamic‑LCA results generation (L3)
The dynamic process inventory inputs are the outcome of
the DCE results and SD simulation findings related to the
LCA calculations. The data flows are represented in D1b
and D2, respectively, in Fig. 3. The DCE model and the SD
simulation outcomes have been used to calculate the LCI in
past studies (Halog and Manik 2011; Onat et al. 2016; Pinto
et al. 2019; Stasinopoulos 2013). The process step formulation and simulation (S3) functions as the central process
step combining DCE model outcomes that are required to
calculate the dynamic process inventory (L2) results in the
C-DLCA framework introduced in Sect. 3.2. Data flow D1b
also represents the DCE outcomes that were required to calculate the functional unit.
The step dynamic-LCA result (L3) is a function of the
dynamic process inventory inputs and an LCA database.
The LCA database is not illustrated in Fig. 3, as it is an
external process. In the C-DLCA framework, the employing
LCA data points from the external database are also required
to represent the dynamically changing life cycle impacts
responding to the temporal changes of the product/service.
An example is a changing shared mobility fleet composition
in a sharing economy system. Based on the powertrain type
composition of the fleet, the data points sourced from the
external LCA database have to be changed. Therefore, unlike
conventional LCA work, the external LCA datasets are timevarying. In the above, the powertrain system composition
is a result of the DCE and SD simulation process, which
is endogenous. However, in the C-DLCA framework, the
external database is assumed to be mutually exclusive from
the system. These LCA data points are multiplied with the
respective dynamic process inventory inputs to generate the
dynamic-LCA results. In general, this step is executed using
LCA software. The final step of the C-DLCA framework is
presented in Sect. 3.9.
3.9 LCIA scenarios and interpretation (L4)
The LCIA calculation process has not deviated from the
conventional approach. Selecting an LCIA method does
not depend on the dynamic-LCA study and follows the
439
conventional approach. The LCIA results are connected
back to the formulation and simulation process (S3) (see
data flow D5 in Fig. 3) to integrate the temporal simulations.
Then, the temporal modelling is performed using the SD
model until the set time horizon in the SD problem articulation (S1) step. The calculated impacts represent dynamic
outcomes influenced by consumer preferences and the technological transformations of the SE assets. Finally, the LCIA
results are re-analysed based on the derived policy scenarios
discussed in Sect. 3.7 and undergo sensitivity analysis to
demonstrate the robustness and minimise deviations from
the actual system behaviour. Then, the results are interpreted
based on the chosen functional unit. The LCIA scenario
analysis concludes the proposed C-DLCA method.
Dynamic characterisation and dynamic normalisation
factors are not considered in the C-DLCA framework to
simplify the proposed methodology. Sohn et al. (2020) highlight only 23% of temporal LCA studies have integrated a
dynamic characterisation process. The integration of the
dynamic characterisation process requires background system expansion and manual modelling by considering the
type of the LCA method, either consequential or attributional. Global warming and toxicity impacts commonly
incorporate LCIA categories in the dynamic characterisation
process (Su et al. 2021). However, it can be challenging to
incorporate the dynamic characterisation process in commonly used LCA modelling software (Sohn et al. 2020).
The dynamic normalisation factor is also rarely integrated
into dynamic-LCAs (Su et al. 2021). Hence, dynamic characterisation and dynamic normalisation factors processes are
not considered in the proposed methodology. Therefore, the
step LCIA and interpretation (L4 in Fig. 3) does not deviate
from the conventional method.
The proposed C-DLCA framework provides an approach
to combining the consumer preferences linked with a
dynamic-LCA of SE systems. It captures the temporal and
causality influences in the life cycle environmental assessment in an SE system by integrating consumer-based and
technological changes in SE assets. This section has elaborated on the application of the proposed C-DLCA framework
representing the proposed process steps in Fig. 3. Section 4
explains a case study application and sets up the model to
calculate the consumer preference integrated dynamic-LCA
results employing the C-DLCA methodology framework.
4 Case study and the system dynamics
model setting
In this section, the C-DLCA framework is applied to check
its applicability and robustness. A shared mobility case study
is chosen to demonstrate the application of the C-DLCA
methodology framework in Sect. 4.1. A summary of the
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market survey and derived results is discussed in Sect. 4.2.
The SD model structure is introduced in Sect. 4.3, and the
user and fleet behaviour sub-model implementations and
results are discussed in Sects. 4.5 and 4.6. This section ends
by generating the dynamic process inventory and setting up
the dynamic-LCA results sub-model.
4.1 Case study selection
A case study is chosen to represent the transition to car-based
sharing economy modes to demonstrate the applicability of
the C-DLCA methodology framework. Multiple factors are
considered whilst selecting the case study. They are (a) significance of the journey type to conduct the market survey, (b)
geographical location and market representation, (c) shared
mobility modes, their availability and usage, (d) fleet technology changes and (e) the availability of the secondary data.
The roundtrip to work has been chosen as it is the most
frequent person journey type in the USA other than nonregular journey types such as shopping and recreational trips
(McGuckin and Fucci 2018). It represents the door-to-door
journey from home to office and the return trip. The USA
was selected as the geographical area of the study based on
its significant changes in use patterns of car-based MaaS
modes (McGuckin and Fucci 2018; Schneider 2021a; US
Census Bureau 2020).
The drove-alone cohort for work increased from 64.4%
in 1980 to 75.9% in 2019, and the number of daily commuting employees increased by 11 million from 2010 to 2019
(US Census Bureau 2020). The USA shared rides usage in
commuting work, including carpooling, makes up 10.3% of
total trips (US Census Bureau 2020). Hence, it is a case that
demonstrates the current car-based MaaS practise. It is the
only journey type that increased from 2009 to 2017, whilst
total commuting trips have decreased by 11% in the USA
(McGuckin and Fucci 2018). An average USA employee
travels 20.6 km daily to/from work (McGuckin and Fucci
2018). The above journey characteristics represent the significance of the roundtrip to work and its contribution to
the light-duty vehicle GHG emission that represents 57% of
the USA transport sector GHG emissions (US EPA 2020).
Three car–based MaaS modes, (a) carpooling, (b) soloridesourcing1 and (c) pooled-ridesourcing, are chosen to
assess the transition of personal mobility in commuting to
work in the USA carpooling (including informally organised
trips) represents the highest car-based MaaS mode of commuting to work trips (US Census Bureau 2020). However,
its usage reduced by 10% from 1980 to 9.8% in 2019 (Office
of Energy Efficiency and Renewable Energy 2016; US
1
Solo-ridesourcing term is employed to highlight the usage of the
ridesourcing mode in non-shared trips.
Census Bureau 2020). On the other hand, solo-ridesourcing
and pooled-ridesourcing have shown an increasing market
share in a few USA cities, such as New York and Chicago,
relative to taxis (Schneider 2021a, b). Also, those relatively
new modes replace taxis in other USA cities demonstrating
higher consumer choice (Rayle et al. 2016). Hence, taxis
are not considered a car-based MaaS mode in this study as
it reduces consumer interest in cities.
To simplify the system structure of the case study, vehicle body style and powertrain type are chosen as the only
two fleet technology attributes. The global electric vehicle
market share reached almost 10 million in 2020 from nearly
zero in 2010 (International Energy Agency (IEA), 2021).
The fleet electrification significantly depends on the government’s policy support (Qian et al. 2019) and the USA has
set to achieve 50% BEV sales by 2030 (The White House,
2021). Shared mobility operators have adopted powertrain
types such as hybrid electric vehicles (HEVs) and BEVs
in their fleets. Uber has a more than five-fold higher HEV
fleet than the private fleet (Uber 2020). Transport network
companies also lead their fleet electrification goals beyond
the state policies and demonstrate their environmental sustainability commitments. Two giant ridesourcing companies,
Uber and Lyft, have pledged to achieve a 100% EV fleet by
2040 and 2030, respectively (Lyft 2020b; Uber 2020).
The transition to sports utility vehicles (SUVs) from
sedan body style is widespread among personal mobility
users. SUVs’ global share has increased from 16.5% in 2010
to 45.9% in 2021 (Cozzi and Apostolos 2021). In the last few
decades, the transition to SUV body type in the USA auto
market also aligned with the global trends (US EPA 2021).
SUVs have increased from 6.9% in 1995 to 23.7% in 2017 in
the USA household–based vehicle distribution (McGuckin
and Fucci 2018). SUV adoption is higher in MaaS modes
such as ridesourcing than the private vehicle fleets. The
use of SUVs in pooled-ridesourcing modes is demanded by
the modes’ nature to cater to many passengers compared to
solo-ridesourcing. Transport network companies demand
larger vehicles for pooled-ridesourcing compared to soloridesourcing (Uber 2021a).
The body style is identified as an endogenous attribute.
This selection is based on the provisions in ridesourcing
Apps to select vehicle body style and not the powertrain type
(Uber 2021b). Hence, an information feedback loop activates
and eventually controls the fleet body style composition in
car-based shared mobility based on the customers’ selection.
Likewise, the powertrain type is selected as an exogenous
parameter. Considering the market trends, gasoline, HEV
and BEV are chosen as the powertrain types. Sedans, SUVs
and hatchbacks are chosen as the vehicle body style to analyse in this work (McGuckin and Fucci 2018; U.S. Energy
Information Administration - EIA 2016, 2019, 2020, 2021;
US EPA 2021, 2018a).
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441
4.1.1 Policy scenarios
The high BEV adoption is selected as an extreme policy
scenario against the baseline scenarios, aligned with the
US president’s executive order on fleet electrification (The
White House, 2021). The anticipated changes to the vehicle body styles are also integrated into these scenarios. The
detailed policy scenarios can be found in Table 1.
4.1.2 Dynamic‑LCA goal
The chosen case study to illustrate the C-DLCA framework
represents a shift from a linear economy–based private vehicle (the business-as-usual scenario) to SE alternatives (in this
work, carpooling, solo-ridesourcing and pooled-ridesourcing).
Since there are two modes to assess in the roundtrip to work
journey in the USA, (a) the private vehicle usage representing
product-based linear economy and (b) car-based MaaS modes
from SE systems, hence, the functional unit of the LCA has
to be chosen to cater for both. As discussed in Sect. 2.1, passenger kilometre (p.km) is selected as the functional unit. The
number of MaaS users is selected as the reference mode in the
dynamic hypothesis. This selection is based on the chosen
functional unit (p.km) and its connectivity with the number
of passengers. Hence, the selected reference mode and the
functional unit complement each other. In addition, the availability of resources, the required accuracy of the results and,
notably, the client(s)/audience(s)’ interests are identified to
determine the scope of the LCA objectively.
The goal of the dynamic-LCA work (step L1 in the C-DLCA
framework) is defined as “analysing the GHG emissions
changes of the transition from private car use to car-based
Table 1 Technology adoption scenarios and parameters
Technology change
Scenario
MaaS mode
Body style
2023–2030
BEV adoption (new vehicles
sales BEV%)
Baseline
CP, PC
Sedan
SUV
Sedan
SUV
Sedan
SUV
Sedan
SUV
Hatch
Sedan
SUV5
Hatch6
Sedan
SUV
Hatch
Sedan
SUV
Sedan
SUV
All
100%d
50%a
a
100%d
50%
b
S
100%
100%d
50%ac
100% from 2026
S
100% from 2026
S
S
Market ­trende
S
Market ­trende
Balance
Balance
Balance
Balance
150% × CP, max 71%
S
3%
2%
20%
S
80%
S
0%
S
See Fig. 11(a) in Appendix
See Fig. 11(b) in Appendix
See Fig. 11(c) in Appendix
See Fig. 11(d) in Appendix
Until 2023—no change
2023 to 2043: based on (Wang et al. 2021)
2043 onwards—sustained
sRS, pRS
SUV adoption
(new vehicle sales SUV%)
High BEV
CP, sRS, pRS
Both scenarios
CP
sRS
pRSg
Vehicles scrappage r­ atesh
Both scenarios
CP
sRS, pRS
Vehicle lightweight
Both scenarios
All
2030–2040
2040–2050
S
S
S
S
S
S
S
Balance
Balance
S
1%
S
S
S
S sustain, a assumption, e estimated
a
b
c
d
e
f
(The White House, 2021)
(Lyft 2020b; Uber 2020)
Assuming SUV electrification is behind compared to the sedan (US EPA 2021, 2018a, b)
Assuming market will follow the USA President’s executive orders (The White House 2021)
Based on the sedan market trend (McGuckin and Fucci 2018) and the fees structure of sRS (Uber 2021b)
Estimated based on (US EPA 2021, 2018a, b)
g
h
Based on the fleet requirement of pRS (Uber 2021c)
Calculated based on survival rates. The combined survival rates are calculated for sedans and SUVs based on (Davis and Boundy 2021; Lu 2006)
13
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sharing economy modes in an average roundtrip to/from work
journey in the USA, considering the consumer preferences and
the fleet technology changes”. Carpooling, solo-ride sourcing
and pooled-ride sourcing have been chosen as the car-based
sharing economy modes and using a private vehicle to work
forms the business-as-usual (baseline) scenario. A full life cycle
(cradle-to-grave) is selected as the scope of the chosen case
study, including the identified vehicle body styles and powertrain types in the fleets. The time scope of the dynamic-LCA
study is chosen from 2014 to 2050. The starting year 2014
represents the year that pooled-ridesourcing, the newest mode
out of the three MaaS modes, is established in the market, and
year 2050, to capture at least two product cycles from 2030,
the year demands 50% new BEV sales in the USA. Section 4.2
presents the conducting of the market survey to capture consumer preferences.
4.2 DCE model set up and results (steps: C1, C2, C3)
The three market survey outcomes, (a) sampling and survey
instrument (C1), (b) DCE model integration (C2) and (c)
survey analysis (C3), are based on (Fernando et al. Under
review). The survey objective was aligned with the goal and
objective (L1) of the dynamic-LCA study and the chosen
case study in Sect. 4.1. Hence, the sampling and survey
instrument were designed to analyse the consumer preference attributes of the roundtrip to/from work in the USA,
considering the use of the private vehicle as the baseline
and carpooling, solo-ridesourcing and pooled-ridesourcing
as the shared mobility modes.
Fernando et al. (Under review) have chosen two fleet
technology attributes. They are (a) rapidly changing vehicle
body style and (b) fleet electrification (McGuckin and Fucci
2018; U.S. Energy Information Administration - EIA 2021,
2020, 2019, 2016; US EPA 2021, 2018a). These are other
than the most influential attributes based on the exploratory research, such as cost, time and number of passengers
(Fernando et al. Under review). The attributes in the DCE
model (process C2 in Fig. 3) are aligned with the identified data fields contributing to calculating dynamic-LCA in
the formulation and simulation (S3) and dynamic process
inventory framework (L2a) processes. The compatibility of
the selected attributes is checked with the formulation and
simulation process (S3) before finalising the DCE model
integration (C2). This extra step is introduced beyond the
approaches represented by Jiang (2019) and Wang et al.
(2016) and ensures the synchronisation of the conjoint analysis results with the SD simulation. The outcomes of the
DCE model are analysed employing MNL, and the results
are presented in Table 2.
The above results show that users of the selected three
modes have shown different consumer preferences on attributes. The results also demonstrate different utilities for the
Table 2 DCE outcomes—utilities
Attribute/level Utility values
Carpooling Solo-ridesourcing Pooled-ridesourcing
Cost (USD)
15
27.00
19
5.25
21
− 10.72
25
− 21.54
40
NA
Time door-to-door (minutes)
15
22.93
20
8.18
25
0.56
30
− 6.06
40
− 25.61
Number of passengers
2
28.50
3
9.60
34
− 20.05
5
− 18.06
Powertrain type
Gasoline
22.62
HEV
3.20
BEV
− 25.87
Vehicle body style
Sedan
23.47
SUV
1.60
Hatch
− 25.08
NA
15.19
11.30
0.08
− 26.57
NA
20.10
11.61
3.42
− 35.13
17.78
10.02
7.87
− 13.71
− 22.97
21.99
10.76
11.01
− 10.52
− 33.26
NA
NA
NA
NA
30.73
− 6.24
− 24.49
0
25.62
− 9.48
− 16.14
26.31
2.59
− 28.91
13.44
14.16
− 27.60
25.53
4.17
− 26.70
Source: (Fernando et al. Under review)
NA not applicable
attribute levels of those modes. In the C-DLCA framework,
the DCE is designed based on the dynamic hypothesis.
4.3 Adoption of the Bass diffusion model
The Bass diffusion model is selected to explain the consumer preferences and the dynamic hypothesis model, considering the generic nature of SE businesses (Zhang et al.
2020). It is a model employed to explain automobile technology changes (Santa-eulalia et al. 2011). A causal loop
diagram that represents the transition from commuting by
private vehicle to shared mobility modes is shown in Fig. 4
by adopting the Bass model. Bass (1969) found that the
market depends on innovators and imitators. Typically, the
innovators adopt based on WoM or other positive feedback
sources. The imitators, instead, are driven by advertising
(Sterman 2000). Two adoption sources WoM and advertising
are assumed to be independent, and the total adoption rate is
expressed in Eq. (3), based on (Sterman 2000).
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443
Fig. 4 Causal loop diagram—the transition to shared mobility modes. Note: adopted from (Bass 1969); variables in green—introduced to integrate the consumer preferences; WoM—word of mouth
Total Adoption Rate (TAR) of SE =
ciPA
+ aP
N
(3)
In this equation, c, i, P, N, a and A denote the contact
rate of SE adopters with potential adopters, SE adoption
fraction, SE potential adopters, total population, advertising effectiveness and adopted population, respectively. The
adoption from WoM is a function of the aforementioned
five factors, and the adoption through advertising is of a
and P. Though P is considered a stock in the Bass model, it
is wholly determined by N and A and can be represented as
an auxiliary variable (Sterman 2000). Hence, the variable P
can be expressed as in Eq. (4).
P=N−A
(4)
The casual loop diagram in Fig. 4 represents the original stock: “Employees commuting by private vehicles” and
the transitioning stock “Active MaaS users”. Two variables,
“Adoption from WoM” and “Adoption from advertising”,
represent the variables that influence the stock levels and
the two feedback loops based on the Bass model (Bass 1969;
Sterman 2000). The key feedback loops are marked as B1
and B2, representing the negative or balancing loops. The
causal loop diagram supports the SD model articulation
and formulation of the dynamic hypothesis (steps S1 and
S2 in Fig. 3). The causal loop diagram in Fig. 4 also helps to
understand the reference mode (the pattern, behaviour over
time (Sterman 2000)) that ultimately leads to the dynamic
hypothesis of the SD model. The number of individuals
transferred to three car-based MaaS modes has been chosen as the reference mode for this illustrative case study,
which can be dynamically hypothesised. The polarity symbol (+ / −) at the end of the arrow represents the influence
of a particular variable.
The SD model implementation is discussed in Sects. 4.4
to 4.7.
4.4 System dynamics sub‑model structure
The sub-models are utilised to explain the deeper information
flows (Sterman 2000). They depend on the defined problem
articulation and the model boundary in Sect. 3.3.2. Figure 5
presents a simplified sub-model to illustrate the data and
information links associated with the chosen case study in
Sect. 4.1. The sub-model arrangement also supports simplifying the formulation of the simulation model steps. The
defined goal of the dynamic-LCA study and the problem
articulation outcome of the SD model (see Sect. 4.1.2) are
considered for deriving these sub-models. Hence, the identified three sub-models are (a) the transition to MaaS, (b)
fleet behaviour and (c) the dynamic-LCA calculation. They
explain the behaviour of the transition model by incorporating consumer preferences, changes in the fleet adoption
13
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Fig. 5 Sub-model diagram for
the SD stock and flow model of
the transition to car-based MaaS
on GHG emissions
based on the powertrain and body style changes and, subsequently, their combined influence on dynamic life cycle GHG
emissions. They are aligned with the LCA and SD model
design defined in Sect. 3.3, and the case study in Sect. 4.1.
The detailed Stock and flow diagram that represents the submodel structure can be found in Fig. 12 in Appendix. Thus,
three sub-models support the execution of the formulation
step aligned with the identified key stocks of the model.
4.5 Model implementation: sub‑model—transition
to MaaS
The model implementation of the transition to car-based MaaS
modes by integrating the market survey–based DCE results
is discussed in this section. Section 4.3 introduced the operationalising of the Bass model (Bass 1969; Sterman 2000), the
chosen model to explain the dynamic hypothesis for the chosen
case study. The feedback systems establishment and integration of the DCE result to calculate the SE system attractiveness
variables are discussed in Sect. 4.5.1. Finally, the transition to
MaaS user behaviour is determined in Sect. 4.5.2.
4.5.1 Integration of survey results and endogenous explanation
The additional feedback loop (MaaS mode attractiveness)
beyond the typical Bass model illustrated in green colour
in Fig. 4 represents the integration of consumer preference
into the model formulation and simulation work. Consumer
decision-making in the SE system does not depend on product acquisition but on its service functionality for temporary
use. Hence, integrating consumer preference in the transition
to active MaaS users is critical and relevant to the dynamicLCA results. The additional feedback loop also represents
the endogenous explanation (vehicle body style) introduced
in Sect. 4.1. Therefore, vehicle body style explains the key
endogenous feedback loop for considering the consumer
preference inputs and is connected to SD modelling work by
employing the variable MaaS mode attractiveness variable.
The product attractiveness concept is employed to integrate
consumer preferences into the SD model. Schmidt and Gary
(2002) have introduced the concept of product attractiveness
and defined it as “the additive utilities of each individual
product attribute”. They have also employed the product
attractiveness concept in SD modelling (Schmidt and Gary
2002). In the proposed C-DLCA methodology, the product
attractiveness conceptually resembles the variable “fraction
willingness to adopt” as introduced by Sterman (2000). He
further argues that the variable “potential adopters” in WoM is
a multiplication of the fraction of willingness to adopt and the
total population. Hence, Eq. (3) is reworked by introducing
Eq. (4), considering the concepts of product attractiveness and
fraction willingness to adopt and combining Eq. (4).
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TAR =
ci(Ej ∗ N − A)A
+ a(N − A)
N
445
(5)
where, Ej denotes ‘mode attractiveness’ (j = carpooling,
solo-ridesourcing and pooled-ridesourcing), the specific
variable to represent product attractiveness in the C-DLCA
method. The primary data collection process to determine
the variable Ej is discussed in Sect. 4.2. The variable carpooling, solo-ridesourcing and pooled-ridesourcing mode
attractiveness combine the consumer behaviour feedback
with the adoption from WoM. As discussed earlier, the feedback from body style is the only endogenous feedback loop
in this simulation.
4.5.2 Transition to MaaS behaviour
Fig. 6 User transition behaviour
(PC, private vehicles; CP, carpooling; sRS, solo-ridesourcing)
Millions
The user transition from private vehicles to car-based MaaS
modes shows an increase based on the model results shown
in Fig. 6. These findings are the outcomes of the formulation and simulation step (S3) of the sub-model transition to
MaaS, the first of three sub-models (see Figs. 5 and 12 in
Appendix). The user transition results show that carpooling is decreasing whilst solo-ridesourcing is significantly
increasing. The model outcomes demonstrate a more noticeable decline among carpooling users from the mid-2020s in
the high BEV adoption scenario. The highest adoption in the
transition to MaaS for the chosen case study is seen in the
low-occupancy-based solo-ridesourcing mode. The results
are aligned with the current ridesourcing market trends of
major cities in the USA (Schneider 2021a, b). The pooledridesourcing behaviour is not discussed separately since its
contribution to the aggregated MaaS is less than 3% at the
end of the modelling period. The increasing number of private vehicle-driven employees in the USA declined compared to the start of the modelling time.
160
The car-based MaaS users show an increase in the high
BEV adoption scenario compared to the baseline. In contrast, to carpooling results, the model outcomes suggest the
highest user uptake in the transition to car-based MaaS in
low-occupancy-based solo-ridesourcing with the high BEV
adoption scenario. The uptake can be further increased if the
BEV utility values are favourable for the ridesourcing (see
Table 2). Therefore, the model findings suggest that the high
BEV adoption scenario attracts more car-based MaaS users
than the baseline scenario in solo-ridesourcing. For private
vehicle use, it works in the other way.
4.6 Model implementation: sub‑model—fleet
behaviour
The expected outcome of this sub-model is to determine the
fleet behaviour of three MaaS modes extending to powertrain type and body style compositions by integrating the
outcomes of the transition to MaaS sub-model discussed in
Sect. 4.5. Hence, the reference mode of this sub-model is
chosen as “fleet stock”. Fleet stock behaviour is modelled
in two components: (a) MaaS fleet behaviour and (b) private
vehicle fleet behaviour. The variables scrap rates are formulated based on the outcomes of the curve fitting exercises
of the vehicle survival rates (Davis and Boundy 2021; Lu
2006). The detailed SD modelling work can be found in
Fig. 11 in Appendix.
The stocks of MaaS fleets (carpooling, solo-ridesourcing
and pooled-ridesourcing) connect the feedback loop between
the transition to MaaS and consumer preference outcomes. This
connection subsequently establishes a feedback loop (causal
relationship) with the variables MaaS fleet body distribution,
vehicle body style attractiveness and MaaS mode attractiveness (see the green loop in Fig. 4). The values for gross occupancy, an exogenous variable that determines the behaviour of
PC
sRS
CP
PC
sRS
CP
140
120
100
Users
80
60
40
20
-
2014
2018
2022
2026
2030
2034
2038
2042
2046
2050
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the MaaS fleet, are sourced from the literature. They are: carpooling—2.18 calculated based on US Census Bureau (2020),
solo-ridesourcing—1, pooled-ridesourcing—2.3, a conservative estimation based on previous studies (Henao and Marshall
2018; Uber 2020; Wilkes et al. 2021). PC to user ratio variable
using the inverse of the occupancy rate of private vehicles to/
from work journey, 1.18 (McGuckin and Fucci 2018). The variable cars per trip defines as the inverse of gross occupancy rates
and distinguishes the vehicle requirements of MaaS modes. It
was assumed that two trips could perform utilising a vehicle in
ridesourcing fleets to cater to/from work trips that usually occur
during peak hours. The temporal fleet technology changes are
integrated through the variable MaaS fleet tech changes and
are based on Table 1.
4.6.1 Fleet behaviour
According to the model results, the personal mobility fleet
shows an increase (see Fig. 7). Carpooling has shown the
largest fleet reduction, whilst solo-ridesourcing shows the
highest fleet increase. The above observations are aligned
with the user preferences results discussed in Sect. 4.5.2.
Hence, the overall fleet stock results highlight that the soloridesourcing fleet dynamics can significantly influence the
dynamic-LCA calculations based on the magnitude of the
fleet increase and the changes in the fleet composition.
The fleet composition changes among mobility modes
show different trends. A considerably larger gasoline fleet
exists in carpooling at the end of the modelling time than
in the other modes. The potential reasons are the increased
automobile longevity based on the survival rates (Davis
and Boundy 2021; Lu 2006), reduced user volumes and no
market mechanism to limit the vehicle age like in ridesourcing modes. The highest fleet increase is seen in the transition to the SUV BEV fleet, whilst the largest reduction is
in gasoline sedans. It also reflects the reinforced consumer
preference for SUVs. Unsurprisingly, the SUV adoption is
significant in solo-ridesourcing compared to sedans, which
is also similar to the carpooling fleet behaviour. The SUVs
represent almost a three-fold increase in 2050 compared
to the 2014 solo-ridesourcing fleet. The fleet behaviour of
hatchbacks is not presented in Fig. 7 and LCA results since
the numbers are significantly lower compared to sedans and
BEVs. This behaviour aligns with the outcomes presented in
Tables 1 and 2. The modelling results also suggest a significantly higher BEV uptake in the solo-ridesourcing fleet from
the mid-2020s onwards whilst flattening and diminishing the
gasoline fleet trajectory. The high BEV adoption scenario
predicts a more considerable uptake of BEVs in all modes
and a larger fleet size. This trend is largely dominated by the
solo-ridesourcing fleet, which is more frequently replaced
to meet the transport network company requirements than
carpooling and private vehicles.
The above observation highlights the importance of considering the powertrain type and body style in the car-based
MaaS fleet behaviour analysis integrating the consumer
preference in the dynamic-LCA calculations. The total fleet
size, powertrain type and body style factors are unique to the
car-based MaaS mode, with the highest contribution from
solo-ridesourcing out of the three chosen modes. Figure 13
in the Appendix shows the stock behaviours of the high
BEV adoption scenario.
4.7 Model implementation: sub‑model—
dynamic‑LCA calculation
The sub-model fleet behaviour (Sect. 4.6) and dynamic process inventory are combined in this sub-model to generate the
final dynamic-LCA results (L3). The results are a combination
of four components: (a) GHG emissions from private vehicles, (b) GHG emissions from MaaS, (c) the functional unit
calculations for p.km and distance-based v.km units and (d)
GHG emissions results component. Components (a) and (b)
are depended on the dynamic process inventory, which is the
temporal version of a (static) LCI in conventional LCA studies.
4.7.1 Establishing the dynamic process inventory
The dynamic process inventory for this work is generated
by combining the (static) LCI data inputs sourced from
the GREET model (Wang et al. 2021). The LCI inputs are
extracted from the GREET model for the chosen fleet technologies established in Sect. 4.6 based on the introduced
temporal changes in Sect. 4.1.1. This approach produces
a dynamic-LCI profile instead of a conventional static
approach (Sohn et al. 2020). Two vehicle body styles—sedan
and SUVs-related GREET model data—are employed, and
90% of sedan impacts are assumed for hatch body style. Passenger car type number one from the GREET model for
sedan and SUV is chosen for this study’s modelling work.
Three powertrain types, (a) gasoline driven, b) HEV and c)
BEV data, are also sourced from the GREET model. In addition, the GREET model-based vehicle lightweighting factors
are also integrated into the dynamic process inventory, as
introduced in Table 1.
The dynamic process inventory is created considering the
three key life cycle phases. The secondary data obtained
from the GREET model for raw materials, production
energy and vehicle lightweighting are utilised to calculate
the dynamic process inventory inputs for the production
phase. The derived vehicle weight values align with the
policy scenarios introduced in Sect. 4.1.1 and are presented
in Table 3. The production phase GHG values and vehicle
lightweighting scenarios are integrated into the production
dynamic process inventory calculations.
13
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The International Journal of Life Cycle Assessment (2023) 28:429–461
(a) 120
Sedan-Gasoline
Sedan-BEV
447
SUV-Gasoline
SUV-BEV
Stock - CP [# vehicles]
60
40
20
0
2014
2022
18
2030
Sedan-Gasoline
Sedan-BEV
SUV-Gasoline
SUV-BEV
16
14
12
10
8
6
4
2
0
2014
2038
(c) 18
Millions
Millions
(b)
80
Stock - sRS [# vehicles]
Stock - Aggregated [# vehicles ]
Millions
100
2022
2030
2038
2046
16
14
2046
Sedan-Gasoline
Sedan-BEV
SUV-Gasoline
SUV-BEV
12
10
8
6
4
2
0
2014
2022
2030
2038
2046
Fig. 7 Fleet behaviour baseline scenario: a aggregated personal mobility fleet, b carpooling fleet and c solo-ridesourcing fleet
The use phase dynamic process inventory changes consist
of three GHG emission components. They are (a) combustion emissions in gasoline and HEV fleets, (b) secondary
emission sources such as refinery emissions and electric
grid and (c) fleet maintenance. Components (a) and (c) are
derived from the GREET model. For component (b), the
USA electricity grid GHG emissions changes are integrated
into the use phase dynamic process inventory calculations
(U.S. Energy Information Administration - EIA 2021, 2019,
2016). The energy required to treat a vehicle at the endof-life and battery recycling impacts are considered in the
dynamic process inventory establishment for the end-oflife phase. The recycled material impacts are not considered since the GREET model follows the secondary material approach, and those impacts are already captured in the
production phase (Wang et al. 2021).
5 Dynamic‑LCA results
This section presents dynamic-LCA outcomes of the transition to MaaS from private vehicles for roundtrips to work
in the US. GHG is chosen as the life cycle impact category aligning with the previous research on shared mobility (Doka and Ökobilanzen 2001; Fernando et al. 2020b;
Greenblatt and Saxena 2015; Nurhadi et al. 2017). The
GREET model is selected as the life cycle database considering a US-based system and specialising in automobilerelated GHG emissions (Wang et al. 2021). The outcomes of
two of the three sub-sections in the C-DLCA methodology
framework application were already discussed in Sects. 4.5
and 4.6. They are combined to generate the dynamic-LCA
results in this section following the implementation steps
established in Sect. 4.7 and the dynamic process inventory
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Table 3 Vehicle weight
excluding the battery
Body style
Powertrain type
Weight
(kg/vehicle)
2014 to ­2019a
Linear weight reduction
(kg/vehicle, (%)) from 2020
to ­2039b
Weight
(kg/vehicle) 2040
­onwardsa
Sedan
Gasoline
HEV
BEV
Gasoline
HEV
BEV
Gasoline
HEV
BEV
1385
1555
1742
1680
1901
2166
1241
1400
1567
217 (15.7%)
268 (17.2%)
357 (20.5%)
249 (14.8%)
313 (16.5%)
435 (20.1%)
195 (15.7%)
241 (17.2%)
322 (20.5%)
1168
1288
1384
1431
1587
1733
1051
1159
1246
SUV
Hatch*
*Assumed 90% of sedans
a
b
(Wang et al. 2021)
Calculated based on (Wang et al. 2021)
introduced in Sect. 4.7.1. The objective, goal and scope setting of the dynamic-LCA were introduced in Sect. 4.1.2, and
p.km was selected as the functional unit of the study. The
C-DLCA methodology framework ends with the LCIA scenario and interpretation component, ensuring that it follows
the ISO14040 principles. The final results are presented in
Fig. 8 in three forms. They are (a) absolute GHG emissions,
(b, c) specific GHG emissions related to the functional unit
p.km and (d) the number of p.km.
The model outcomes show a significant reduction in
aggregated personal mobility-related dynamic-GHG emissions for the roundtrip to work in the USA by transitioning
to car-based MaaS at the end of modelling compared to the
beginning in both scenarios (see Fig. 8a). The dynamic-GHG
results also reflect the influence of final results based on the
user preferences and fleet composition findings presented
in Sects. 4.5 and 4.6. The steep decline in absolute GHG
emissions starting from the mid-2020s represent the influence of fleet electrification based on the policy scenarios
introduced in Sect. 4.1.1. The high BEV adoption scenario
shows an increase starting from 2022 and gradually declining. This trajectory represents the contribution of higher production emissions during the aggressive fleet electrification
to achieve 100% BEV sales in 2030 (the life cycle phase
implications are discussed in detail in Sect. 5.1). Hence,
this observation demonstrates that the model responds to
the established policy scenarios in Sect. 4.1.1.
The overall GHG emission reduction in the high BEV
adoption scenario compared to the baseline is significant.
Still, it does not provide a complete solution to the personal
mobility-related GHG emissions for the chosen case study
(see Fig. 8a). These outcomes are against the expectations
of the transport network companies (Lyft 2020b; Uber
2020) that only consider fleet technology changes such as
electrification. However, the integration of consumer preferences reflects the influence of market response in the
dynamic-LCA results. Therefore, it is worthwhile to analyse
the dynamic-GHG emissions of mobility modes.
As shown in Fig. 8a, carpooling mode predicts significant
GHG emissions savings at the end of modelling compared
to the beginning. It also shows higher GHG emissions savings in the high BEV adoption scenario than in the baseline.
Unsurprisingly, the high BEV adoption scenario carpooling
shows the least specific GHG emissions in the transition
to car-based MaaS modes supported by the higher p.km
volumes based on the highest occupancy rate. Hence, this
result highlights the importance of promoting high occupancy shared mobility modes in reducing GHG emissions
in personal mobility. The model results also show the overall
carpooling p.km units decreased at the end of the modelling
period compared to the beginning. The above finding aligns
with the declining user behaviour discussed in Sect. 4.5.2.
Model results suggest a significant increase in the absolute GHG emissions in the solo-ridesourcing mode in both
scenarios. Interestingly, the absolute GHG emissions of soloridesourcing of the high BEV adoption scenario are larger
than the baseline scenario. This trend can be explained based
on the higher utility values for BEVs in solo-ridesourcing
fleets (see Table 2) and resulting in a larger fleet in the high
BEV adoption scenario compared to the baseline scenario
(see Fig. 7c). Both faster fleet replacement rates and higher
electrification rates to satisfy the transport network company
requirements significantly change the GHG emissions trajectory of solo-ridesourcing fleets. However, the reduction is
significantly steeper than the other modes in specific GHG
results (see Fig. 8c). The above observation demonstrates
the significance of fleet technology changes in reducing
specific GHG reduction. Due to the highest user transition
(see Fig. 6), the p.km volumes of solo-ridesourcing record
a higher number. Hence, further optimisation of p.km can
bring down the specific GHG emission reductions. The above
results reinforce the importance of optimising the occupancy
13
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rate to achieve better GHG results in car-based shared mobility modes. The results also confirm the choosing p.km as
an effective functional unit to compare against the modes.
Pooled-ridesourcing also shows similar behaviour to soloridesourcing with a significantly lower magnitude. Hence,
its variations are not discussed explicitly.
Figure 8(a) also shows that the absolute GHG emissions
of the aggregated MaaS modes increased significantly, at
least threefold, from 2014 to 2050. The aggregated MaaS
GHG emissions per p.km in 2050 do not show a reduction in
both scenarios compared to 2014 (Fig. 8b). The shift to the
higher specific GHG emitting solo-ridesourcing mode and
the reduction of lower-specific GHG emitting carpooling
users contribute to the increasing GHG emission per p.km.
The emissions changes are also supported by the transition
to the dominant SUV fleets, especially in two ridesourcing
modes. Two modes’ occupancy rates also conversely contributed to the emission changes. Revealing these findings
would not have been possible if the consumer preferences
and temporal changes were not integrated. In all mobility
modes, both aggregated and individual, the high BEV adoption scenario shows lower GHG emissions compared to the
baseline scenario. Hence, the specific GHG results show the
importance of fleet electrification in the overall transition
to the car-based MaaS from private vehicles. However, it is
not providing a complete solution to the GHG emissions for
personal mobility in roundtrips to work in the USA.
5.1 Results in life cycle phases
As shown in Fig. 9c, the model predicts a significant GHG
reduction in the use phase and a little more reduction in the
high BEV adoption scenario compared to the baseline scenario at the end of the modelling period compared to 2014.
The historical steady increase started to decline in the mid2020s when the BEV transition was activated. The further
reduction in the mid to later modelling period is supported
by vehicle lightweighting and the USA electric grid GHG
emissions reductions. Altogether, by 2050, the use phase
GHG emission contribution is around half of the overall
GHG emissions. The private vehicle fleet contributes the
highest GHG emission in the use phase (see Fig. 9d), nearly
90%. However, a significant observation towards the end
of the modelling period is reducing the contribution of the
private vehicle fleet to the use phase GHG emissions.
In contrast, the solo-ridesourcing use phase GHG fraction increased significantly, reaching nearly one-fifth at the
end of the modelling period, which was initially insignificant. The use phase GHG emissions of the carpooling mode
reduced by the end of the modelling. These observations
align with the user preference trends discussed in Sect. 4.5.2
and subsequent fleet behaviour in Sect. 4.6.1. A key factor
for the GHG emissions changes in solo-ridesourcing and
449
carpooling modes is the gross occupancy rates and the deadheading factors (Fernando et al. 2020b, 2020b; Henao and
Marshall 2018; Union of Concerned Scientists 2020b). The
gross occupancy rates of solo-ridesourcing are significantly
lower than carpooling, and the mode records the highest
deadheading among the chosen car-based mobility modes
(Henao and Marshall 2018; Union of Concerned Scientists
2020a). Hence, the aggregated car-based MaaS modes contribute to a higher use phase GHG emission fractions at the
end of the modelling time, mainly dominated by the soloridesourcing mode.
In contrast to the use phase GHG emissions, the production phase emissions show an increase (see Fig. 9a). In the
high BEV adoption scenario, the production GHG emissions
show an increase from 2022, the year that starts replacing
gasoline vehicles rapidly, to achieve 100% BEV purchase
by 2030 (see Sect. 4.1.1). The higher GHG emissions from
the BEV raw material composition compared to internal
combustion engine vehicles, especially from the battery, is
a key reason for this increase (Wang et al. 2021). The fleet
purchasing behaviour and the vehicle production GHG emission rates determine the production phase GHG emissions.
The composition of private vehicle purchases significantly
changes from gasoline sedans and SUVs to more BEV-driven
vehicles, particularly SUVs, in the late 2030s (see Fig. 14
in Appendix). The higher GHG emissions in the production
phase due to fleet electrification can be seen by comparing
graphs (a) and (b) in Fig. 9. The highest production phase
GHG emissions are recorded when the complete transition
to BEVs with a higher composition of SUVs, demonstrating
a node by the end of the 2030s. The consumer preferences
determine the vehicle purchasing composition (based on the
derived utility values in Sect. 4.2), and the results of the SD
sub-model transition to MaaS and replacements for the scrappages are also determining factors in fleet purchasing.
As shown in Fig. 9b, other than private vehicles, the highest GHG contribution is from the solo-ridesourcing fleet. In
the solo-ridesourcing production phase GHG emissions are
increased because of the higher purchasing and scrappage
rates to maintain the vehicle age restrictions enforced by the
transport network companies (Uber 2021a). The combination
of larger BEV and SUV fractions in the ridesourcing modes
is another explanation for the rise of production phase GHG
emissions. Both BEVs and SUVs generate larger production phase GHG emissions than internal combustion engine
vehicles and body styles such as sedans (Wang et al. 2021).
The model results predict the significance of the production phase GHG emissions in the future. It is contributed by
several factors such as raw material embodied emissions due
to lightweighting and fleet electrification, transition to SUVs
from sedans, and consumer preference-influenced changes
in the user compositions in car-based mobility fleets.
This explanation highlights the importance of integrating
13
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The International Journal of Life Cycle Assessment (2023) 28:429–461
150
Baseline CP
Baseline PC
High BEV sRS
Baseline sRS
High BEV CP
High BEV PC
2038
2046
125
GHG emission [tCO2eq/annum]
Millions
(a) 175
100
75
50
25
0
2014
2018
(b) 500
2026
Baseline Aggregated MaaS
High BEV Aggregated MaaS
Baseline Aggregated
High BEV Aggregated
450
400
350
2030
300
250
200
150
100
Billions
(d)
2042
450
400
50
0
2034
(c) 500
GHG emission [gCO2eq/p.km]
GHG emissions [gCO2eq/p.km]
2022
2050
CP
CP
sRS
sRS
PC
PC
350
300
250
200
150
100
50
2014
2022
2030
2038
0
2046
2014
2022
2030
2038
2046
800
700
600
[p.km]
500
400
300
200
100
0
Baseline CP
High BEV CP
2014
2018
2022
2026
Baseline sRS
High BEV sRS
2030
2034
Baseline PC
High BEV PC
2038
2042
2046
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2050
The International Journal of Life Cycle Assessment (2023) 28:429–461
◂Fig. 8 Dynamic-GHG results: a absolute GHG emissions, b specific
GHG emissions compared to p.km – aggregated fleets, c specific
GHG emissions compared to p.km—individual modes and d p.km
consumer preferences, technology changes, temporality, and
their causal interactions in the sharing economy systems in
assessing GHG emissions.
Baseline Prod
High BEV Prod
20
(b)40
10
Prod-PC
Prod-sRS
Prod-CP
Prod-pRS
30
10
2014
2022
2030
2038
Baseline Use
High BEV Use
125
100
2022
2030
(d) 150
2038
Use-PC
Use-CP
2046
Use-sRS
Use-pRS
125
GHG emission [tCO2eq/annum]
100
75
50
25
0
0
2014
2046
Millions
Millions
The C-DLCA framework has provided a structured and practical approach to integrating consumer preference outcomes
into the SD model, considering the dynamic-LCA calculation
20
(b) 150
GHG emission [tCO2eq/annum]
6.1 The C‑DLCA model application summary
GHG emission [tCO2eq/annum]
GHG emission [tCO2eq/annum]
30
0
6 Discussion
Millions
Millions
(a) 40
451
2014
2022
2030
2038
2046
75
50
25
0
2014
2022
2030
2038
2046
Fig. 9 Dynamic-GHG results—life cycle phases in the baseline scenario. a Production phase—aggregated emissions, b production phase—individual
modes, c use phase—aggregated emissions and d use phase—individual modes
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452
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Fig. 10 Consumer preference
integrated dynamic-LCA results
versus not combining it (the
baseline scenario results)
Millions
as the end goal. The framework effectively facilitated the
integration of inputs and outputs of the three models: (a)
consumer preference measured by DCE, (b) temporality and
causality integrated using SD as the interface and (c) environmental impact assessment by employing LCA. The complex
handling of the process step formulation and simulation (S3
in Fig. 3) would have been impossible without such a simplified and straightforward framework. The step S3 itself,
handled four data flows (D1a, D2, D4 and D5). The methodology framework demonstrates its robustness by the effectual
application in the selected complex case study.
Based on the chosen case study, the established C-DLCA
feedback flows were well utilised and demonstrated the
model’s capability to determine the life cycle GHG emissions of the transition to car-based MaaS modes of the
roundtrip to work journey in the US. As shown in Fig. 5,
the sub-model diagram and the derived causal loop diagram
based on the Baas diffusion model (see Fig. 4) combine
consumer preference and fleet technology feedback in the
systems modelling. The selected shared mobility-based case
study is complex in terms of considering several shared
mobility modes and their fleet technology implications. The
technology consideration is further extended to the changes
in automobile production aspects. They are lightweighting
and extended longevity (based on the survival rates). Indirect
influences such as the electric grid GHG emissions changes
are also considered in the dynamic-LCA calculations.
Hence, applying the C-DLCA framework in the chosen case
study is closer to the market reality. The application of the
framework in the case study also indicates the replicability
of the C-DLCA in other SE systems. It highlights the importance of integrating consumer preference and temporality in
the environmental evaluations of SE systems.
Though the C-DLCA methodology framework is designed
for SE systems, with brief customisations, it can also apply
to environmentally evaluate the linear economy systems. The
only foreseen difference is the changes in the DCE model
to change the attributes to represent linear economy-centric
consumer preferences.
6.2 Effect of consumer preference
A significant gap can be seen in the dynamic-GHG results
in the two conditions: (a) considering consumer preference
and (b) not considering. In a typical dynamic-LCA work,
only the temporal changes influence the results compared
to a conventional, static LCA (Sohn et al. 2020). Figure 10
shows the generated results combining consumer preference
in dynamic-GHG emissions (as discussed in Sect. 5) and
the conventional dynamic-LCA approach without considering consumer preferences. The graphical representation
of “not considering consumer preference” is generated by
simplifying the sub-model transition to MaaS introduced
in Sect. 4.5. A simple, linear formulation is introduced to
car-based MaaS modes instead of the Bass diffusion modeldriven causality explanation. Hence, the results “not considering consumer preference” shown in Fig. 10 only characterise the temporal fleet technological attributes (powertrain
type and vehicle body style compositions, lightweighting
and longevity improvements, and the USA electric grid
GHG changes) as introduced in Sect. 4.1.1. Therefore, a
significant GHG emission gap is determined between the
integrated DCE results (i.e., consumer preference combined,
as in this work) findings versus the typical approach of not
considering them.
Figure 10 shows a significant deviation in GHG emissions
considering consumer preferences against those not considering. The expected GHG reduction, considering only the
fleet technology changes, shows a significantly higher GHG
emission reduction in personal mobility compared to the
results shown in Sect. 5. The difference in GHG emissions
outcomes highlights the significance of consumer preference
200
175
Personal mobility fleet
Personal mobility fleet
Aggregated MaaS
Aggregated MaaS
150
GHG emission [tCO2eq/annum]
125
100
75
50
25
0
2014
2018
2022
2026
2030
2034
2038
2042
13
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2046
2050
The International Journal of Life Cycle Assessment (2023) 28:429–461
integration in the environmental consequences of the technology changes. Hence, the results show the importance of
combining consumer preference in the dynamic-LCAs in
the transition to car-based sharing economy modes. A GHG
emission reduction is expected without consumer preference integration, considering the anticipated fleet technology changes introduced in Sect. 4.1.1. However, consumer
preference integration significantly changes the car-based
MaaS composition considering the fleet-technology offerings. Another observation is that only the technology-based
uncertainties would have been analysed if consumer preference were not integrated into the analysis. Hence, the sensitivity of the uncertainty associated with consumer preferences and other sensitivity elements is limited if not applying
the C-DLCA framework and assessing by only employing
the dynamic-LCA approach. These findings also align with
the outcomes of Sects. 4.5 and 4.6, particularly the consumer
preferences in different MaaS modes. In this work, the vehicle body style was only chosen as the endogenous explanation (see Sect. 4.3). Therefore, the above results highlight the
significance of combining consumer preference in dynamicLCA analysis in SE systems, and the practical and robust
application of the C-DLCA methodology framework.
7 Conclusion
This paper presented a new methodology framework to integrate consumer preferences into the dynamic-LCA of sharing economy systems. The life cycle environmental impact
assessments of SE systems required a robust method to
integrate consumer-based decisions and temporal technological changes in their asset pools. The influence of consumer preferences in SE systems is significant compared
to linear economy systems. This paper explored consumer
preferences and its dynamic and causal influences in the life
cycle environmental assessments of SE systems compared
to linear economy models. The C-DLCA framework is proposed to assess SE systems, combining the technological
changes in asset pools. The full life cycle scope is chosen
to determine the differences in SE asset pools to integrate
the features: achieving product longevity within a shorter
period, adopting newer technologies, and different production and disposal patterns. Selecting a service-based functional unit (i.e., p.km in this study) is identified as a must in
effectively interpreting the dynamic-LCA results of an SE
system and for effective comparison against the respective
linear economy system.
The SD method is used as the interface to combine consumer
preference dynamics and the LCA method. Sterman’s five process steps (Sterman 2000) is selected as the SD approach, and
its iterative characteristics are integrated with the proposed
methodology. Previous research has not combined consumer
453
preferences, SD model and LCA to assess the systems through
SD models (Jiang 2019; Wang et al. 2016), and SD and LCA
modes (Stasinopoulos et al. 2012) were conducted separately.
A novel methodology approach and structured framework,
C-DLCA, is introduced to connect the consumer preference
integrated SD model with the LCA method. The proposed
methodology has provided a step-based approach connecting
the different phases of the three methods (DCE, SD and LCA)
and established the feedback and data loops. In the C-DLCA
framework, the step “formulation and simulation” combines
the conjoint analysis-based DCE results, LCI and LCIA outcomes into the SD model. This connectivity added temporal
and causal influences to the dynamic process inventory inputs
for the LCI and then derived the LCIA results. The testing and
policy scenarios steps of the SD approach generate the LCIA
scenarios to be re-evaluated.
This is the first study that quantified the combined impacts
of consumer preferences, technology changes and their causal
influences on the life cycle environmental impacts. The outcomes of the case-study-based C-DLCA methodology framework demonstrate the significance of integrating SE consumer
preferences into a dynamic-LCA. In SE, integrating consumer
preference is critical to gathering information directly from
stakeholders (consumers specifically) to understand the temporal effect in the analysis. The proposed C-DLCA framework
has provided a solution to integrate consumer preferences,
technological changes in SE assets and their dynamics into
the life cycle environmental impact assessments of SE systems in a single formulation. The Bass model (Bass 1969) is
adopted to simulate the transition to SE systems. MNL modelbased, logit choice probability results are utilised to represent
the SE mode attractiveness as a probabilistic function. The
most influential attributes are incorporated into the consumer
preferences study and are classified as either endogenous or
exogenous based on the scope of the study. The vehicle body
style is chosen as the endogenous variable in this work based
on the chosen case study. A robust and structured integration
of utilising the SD interface has also added some standardisation to the dynamic-LCAs of the SE systems. The above
capabilities of the proposed C-DLCA framework have been
proven with the illustrated complex case study on the transition to car-based shared mobility modes of the roundtrip to
work journey in the US. Hence, the C-DLCA framework is
suitable for environmentally assessing the SE modes.
The attribute powertrain type was not considered endogenous since consumers do not have the choice option to select
it in current shared mobility Apps. However, with the BEV
promotion in the ridesourcing fleet, the option to select the
powertrain type would be generic in the future. Therefore,
the powertrain type can be considered an endogenous attribute identified as a potential future work. It can add increased
research value with the speed of changes in the electrification of the personal mobility fleet Fig. 11, 12, 13, and 14.
13
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The International Journal of Life Cycle Assessment (2023) 28:429–461
Appendix
Fig. 11 Calculated cumulative
vehicle scrappage rates. Note:
a sedan and hatch—carpooling
and private fleets, b SUVs—
carpooling and private fleets,
c sedan and hatch—ridesourcing and d SUVs—ridesourcing; assuming the vehicle was
purchased in 2014, based on
the vehicle survival data (Davis
and Boundy (2021, pp. 3–20)
and (Lu (2006, p. 8); for c, d,
assuming the maximum age of
a vehicle in a ridesourcing fleet
is 15 years (Uber 2021c) and no
second life
(a)
(b)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2014
init
2018
2023
2028
2033
2038
(d)
(c)
100%
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
2014 2018 2023 2028 2033 2038 2043 2048
init
2014 init
2016
2019
2022
0%
2014 init
2016
2019
13
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2022
The International Journal of Life Cycle Assessment (2023) 28:429–461
455
Fig. 12 Stock flow diagram of the transition to MaaS dynamic-GHG. Notes: key to colour codes
13
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Millions
(a) 120
Sedan-Gasoline
Sedan-BEV
SUV-Gasoline
SUV-BEV
100
60
40
20
0
2014
2022
(b) 18
2030
Sedan-Gasoline
Sedan-BEV
SUV-Gasoline
SUV-BEV
Millions
16
14
(c) 18
16
14
10
10
Stock - sRS [# vehicles]
12
[Stock CP # vehicles]
12
8
6
4
2
0
2014
2022
2030
2038
2046
2046
2038
Millions
Stock - Aggregated [# vehicles]
80
Sedan-Gasoline
Sedan-BEV
SUV-Gasoline
SUV-BEV
8
6
4
2
0
2014
2022
2030
2038
2046
Fig. 13 Fleet behaviour high BEV adoption scenario: a aggregated personal mobility fleet, b carpooling fleet and c solo-ridesourcing fleet
13
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The International Journal of Life Cycle Assessment (2023) 28:429–461
(a)
25
GHG emission [tCO2eq/annum] Millions
Fig. 14 Private vehicles—
production phase. a DynamicGHG emission results based on
fleet technology composition—
baseline scenario and b private
vehicle purchasing trends based
on the key fleet technology
composition—baseline scenario. The yellow highlighted
area represents the complete
transition to BEVs
20
Others
SUV-Gasoline
Sedan-Gasoline
Sedan-BEV
SUV-BEV
15
10
5
0
2014
(b) 8
2018
2022
Baseline
7
Millions
457
2026
2030
Baseline
2034
2038
38
Baseline
2042
Baseline
B
ase
2046
2050
Baseline
6
5
[No. of vehicles]
4
3
2
1
0
2014
2018
2022
Funding Open Access funding enabled and organized by CAUL and
its Member Institutions. This study is supported by the ARC Training Centre in Lightweight Automotive Structure (project number
IC160100032) and the Australian National University and is funded by
the Australian Government. The authors sincerely appreciate valuable
comments and suggestions from the reviewers that helped to improve
the manuscript.
Data availability None.
Declarations
Conflict of interest None.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.
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Publisher's Note Springer Nature remains neutral with regard to
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Authors and Affiliations
Chalaka Fernando1
· Gary Buttriss2 · Hwan‑Jin Yoon3 · Vi Kie Soo1,4 · Paul Compston1 · Matthew Doolan1,5
1
Australian Research Council Training Centre in Lightweight
Automotive Structures, The Australian National University,
Canberra, ACT​2601, Australia
2
College of Business and Economics, The Australian National
University, Canberra, ACT​2601, Australia
3
Health Intelligence, The Australian E-Health Research
Centre, CSIRO, Parkville, VIC 3052, Australia
4
Thinkstep-Anz, Regus Offices, Level 5, 616 Harris Street,
Ultimo, NSW 2007, Australia
5
School of Engineering and Information Technology, UNSW
Canberra, Canberra ACT 2600, Australia
13
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