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Consumer Behaviour Integrated Dynamic Life Cycle Assessment of Car-based Sharing Economy

Fernando, Chalaka

Description

The personal mobility modes such as private vehicles contribute significantly to the increasing greenhouse gas (GHG) emissions. The car-based sharing economy modes are considered a solution to reducing personal mobility-related GHG impacts. These car-based Mobility as a Service (MaaS) modes have lowered the barrier of affording the vehicle usage compared to the product-based linear economy, thus changing the consumer decision paradigm. Their fleets have also transformed technologically to face...[Show more]

dc.contributor.authorFernando, Chalaka
dc.date.accessioned2023-02-19T12:14:29Z
dc.date.available2023-02-19T12:14:29Z
dc.identifier.urihttp://hdl.handle.net/1885/285300
dc.description.abstractThe personal mobility modes such as private vehicles contribute significantly to the increasing greenhouse gas (GHG) emissions. The car-based sharing economy modes are considered a solution to reducing personal mobility-related GHG impacts. These car-based Mobility as a Service (MaaS) modes have lowered the barrier of affording the vehicle usage compared to the product-based linear economy, thus changing the consumer decision paradigm. Their fleets have also transformed technologically to face increased pressure to incorporate environmental responsibilities. The MaaS consumer preferences can influence these fleet changes. This thesis quantifies the environmental impacts of the transition to MaaS from private vehicles considering consumer preferences and their feedback on timely varying fleet technological changes. The Consumer behaviour integrated Dynamic-Life Cycle Assessment methodology framework is introduced as a structured approach to integrating MaaS consumer behaviour into a Dynamic-Life Cycle Assessment (Dynamic-LCA). It employs System Dynamics (SD) technique as the modelling interface. The full life cycle is chosen as the LCA scope. The US GREET model-based lightweighting and life cycle inventory and the US electric grid factor GHG changes are incorporated in the modelling. A scenario is chosen based on the adoption of the High Battery Electric Vehicle (BEV). The US roundtrip to work, the most frequent and increasing routine journey type, is chosen as the case study to analyse the consumer preferences and decision-making attributes for private vehicles, carpooling, ridesourcing and pooled-ridesourcing. A significant market survey was administered with 2,968 qualified samples representing geography and transit ridership. Cost, journey time, number of passengers, body style, and powertrain type were chosen as the Choice-Based Conjoint Analysis's attributes to integrate the feature of the Discrete Choice Experiment (DCE) and MaaS modes. Results show that cost, journey time, and occupancy are significant attributes, and powertrain type is the least. Also, utility outcomes are towards more environmentally unsustainable gasoline and SUVs. Hence, results show a value-action gap associated with transitioning to MaaS. The Bass model (Bass, 1969) is adopted to simulate the transition to MaaS by incorporating the DCE outcomes, and the vehicle body type provided the endogenous explanation in the SD modelling. The Dynamic-LCA outcomes show a significant GHG emissions reduction in aggregated personal mobility and a further reduction in the High BEV adoption scenario. Results suggest a decline in carpooling, the least GHG emitting car-based MaaS mode. The aggregated MaaS GHG emissions are considerably increased due to the deadheading contribution from the extensively increasing, BEV backed solo-ridesourcing fleet. The High BEV adoption scenario attracts more MaaS consumers while emitting more GHG emissions from SUV prominent solo-ridesourcing. The dynamic fleet technology changes increase manufacturing and End-of-Life GHG emissions while reducing use phase emissions with the support of vehicle lightweighting, increasing survivability, and US electric grid GHG factor. This research highlights the shift to BEV driven pooling modes to reduce life cycle GHG emissions in personal mobility. It also highlights the significance of combining consumer preference and dynamic fleet technology changes to determine GHG emissions in the transition to sharing economy based car modes.
dc.language.isoen_AU
dc.titleConsumer Behaviour Integrated Dynamic Life Cycle Assessment of Car-based Sharing Economy
dc.typeThesis (PhD)
local.contributor.supervisorDoolan, Matthew
local.contributor.supervisorcontactu9801883@anu.edu.au
dc.date.issued2023
local.identifier.doi10.25911/ZHG2-SH07
local.identifier.proquestYes
local.identifier.researcherIDGQZ-6079-2022
local.thesisANUonly.authora981f77b-5828-47e0-90f3-a143cea378e0
local.thesisANUonly.title000000021467_TC_1
local.thesisANUonly.key61bbdf16-6c42-5947-9325-28bb70b2cd05
local.mintdoimint
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