Diffusion of alternative fuel vehicles considering dynamic preferences

https://doi.org/10.1016/j.techfore.2019.06.002Get rights and content

Highlights

  • Alternative Fuel Vehicles (AFVs) diffusion model incorporates dynamic preferences.

  • A Static Preferences model and a Dynamic Preferences model are compared.

  • AFVs adoption results from Dynamic Preferences model are markedly different from the Static model.

  • Degressive subsidies allow achieving higher market penetration of AFVs than constant subsidies.

Abstract

Consumer preferences are a crucial element of models aimed at understanding and predicting the diffusion of Alternative Fuel Vehicles (AFVs). Previous AFVs diffusion studies have considered static preferences, but preferences for complex products such as AFVs are likely to change under different market conditions. Therefore, using static preferences for demand forecasts may compromise the accuracy of those predictions. This study aims at incorporating dynamic preferences on a reference AFVs diffusion model and analyzing if adapting subsidy policies according to those preferences will provide more cost-effective results on AFVs adoption. A System Dynamics model adapted to the Portuguese market was developed to study the impact of considering dynamic preferences and several incentive policies adapted to such preferences.

Two system dynamics models are developed for comparative purposes: one considering static preferences and other one considering dynamic preferences. According to the results derived from these models, the model with dynamic preferences predicts a higher market penetration of AFVs, mainly due to the increment of Plug-in Hybrid Electric vehicles and Battery Electric Vehicle market shares. These results show that considering dynamic consumer preferences has a significant impact on AFVs diffusion predictions. The subsidies scenarios allow concluding that designing subsidies according to the evolution of preferences stimulated AFVs adoption more effectively.

Introduction

Road transportation is still a matter of great concern, accounting for more than a fourth of the total energy consumption and for two-thirds of the European Union final demand of oil and its derivatives (European Commission, 2018). Alternative Fuel Vehicles (AFVs) have been regarded as possible solutions for energy use and environmental problems, using alternative energy sources and potentially reducing greenhouse gas emissions (Hacker et al., 2009). Their contribution comes not only from the use of more efficient engines than conventional vehicles but also from the possibility of using renewable energy to charge electric batteries (Hacker et al., 2009). However, AFVs have had difficulties to penetrate the markets, as consumers continue to have technical and economic concerns about the adoption of new vehicle technologies (Hidrue et al., 2011; Potoglou and Kanaroglou, 2007). Indeed, several barriers strongly affect the transition from conventional vehicles to AFVs, such as their limited range and the (un)availability of charging infrastructures, not to mention consumers resistance to adopting innovative technologies (Leiby and Rubin, 2004).

Several diffusion studies have tried to understand AFVs market penetration in order to predict consumer behavior in face of the introduction of these vehicles. Diffusion analysis is particularly suited to identify measures to overcome market barriers, addressing the process of innovation diffusion (Rogers, 1962). One of the main purposes of the diffusion studies for AFVs is to forecast vehicles demand (e.g., Janssen et al., 2006; Keles et al., 2008; Köhler et al., 2010; Kwon, 2012; Park et al., 2011; Shepherd et al., 2012; Walther et al., 2010). In these studies consumers play a major role by providing the stated preferences required to support the prediction of that demand for new vehicle technologies (Ahn et al., 2008). Their preferences are considered a critical factor for the success of AFVs development (Huijts et al., 2012; Struben and Sterman, 2008). Traditionally, in the economics field, preferences were considered as static limiting analysis of the consequences of a given set of preferences (Janssen and Jager, 2001). Currently, economics joined the psychological and marketing fields, in which questions such as how the preferences are formed and how they change over time are addressed (Janssen and Jager, 2001; Lachaab et al., 2006).

As consumer preferences towards more complex products, such as AFVs, are less stable (Bettman et al., 1998), several researchers analyzed the preferences for these vehicles and concluded that consumer preferences for AFVs were likely to change under different market conditions, i.e., they were dynamic (Axsen et al., 2009; Maness and Cirillo, 2012; Mau et al., 2008). Therefore, ignoring the evolution of preferences may lead to inaccurate predictions of vehicle market shares, especially when the measurement of preferences is done well ahead of the forecast period (Axsen et al., 2013; Meeran et al., 2017). Dynamic preferences are an important component of technological change that should not be left out from new vehicle technologies analysis (Axsen et al., 2009).

The literature on consumer preferences (reviewed in Section 2.1) sustains that these preferences evolve in time, i.e., they are dynamic. However, only static preferences have been considered in AFVs diffusion studies literature (reviewed in Section 2.2). Therefore, the main contribution of the present research is to incorporate dynamic consumer preferences on an AFVs diffusion model in order to assess their impact on the market penetration of these vehicles. This is an innovative approach on the diffusion analysis of AFVs as not only the attribute values change over time but the consumer preferences for each attribute also change. In this study, dynamic preferences are seen as a consequence of changes in the market conditions. This means that preferences change with different economic environments that imply changes in social interactions between consumers and their relatives or friends that may or not have experienced the product. The study addresses the Portuguese market, in which the penetration of AFVs has been particularly hard (Section 4), and it is focused on the Electric Vehicles (EVs) already available in this market, namely Battery-Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Hybrid Electric Vehicles (HEVs).

The implementation of government policies is a frequently analyzed strategy to influence consumer preferences, by which governments seek to foster the rapid diffusion of environmental friendly technologies (Soete and Arundel, 1995). Monetary policies are among the most commonly studied, since one of the consumer's main concerns is the financial burden associated with buying a vehicle. Therefore, monetary incentives consisting in an up-front discount on the vehicle purchase price have the potential to positively influence consumers' vehicle purchase decisions (Eggers and Eggers, 2011; Borthwick, 2012). However, the effectiveness of purchase subsidies is not easy to predict as consumers may consider that AFVs remain too expensive even with a price reduction (Bakker and Trip, 2013). The Portuguese government implemented several policies in order to stimulate the adoption of AFVs, including a BEV purchase subsidy of 5000€. However, sales were far below the initial projections (ACAP, 2013). Within this context, an interesting research question is how to design an incentive policy that would be adapted to the evolution of dynamic consumer preferences. Therefore, the second contribution of this study is to assess if adapting subsidies to dynamic consumer preferences can provide more cost-effective results. The analysis of the impact of subsidies adapted to dynamic preferences challenges the established practice of analyzing the impact of purchase subsidies on the diffusion of AFVs, as the impact of purchase incentives has always been analyzed assuming the implementation of constant subsidies.

A System Dynamics (SD) model adapted to the Portuguese market was developed to study the impact of considering dynamic preferences and several incentive policies adapted to such preferences.

The paper is organised as follows. The next section presents a review of studies that addressed the existence of dynamic preferences and the state-of-the-art of modelling consumer preferences in AFVs diffusion studies using SD. The SD model developed for this study is described and justified in Section 3. Section 4 presents the SD diffusion model and its validation and calibration for Portugal. Results and main conclusions are reported on 5 Results, 6 Conclusions, respectively.

Section snippets

Review about dynamic consumer preferences

In the past few years several studies aimed at verifying if consumer preferences were dynamic. Lachaab et al. (2006) analyzed the evolution of preferences regarding an unnamed packaged good. Using an eight year panel data of household purchases they concluded that preferences for product attributes changed over time, e.g. consumers became more price sensitive over time. Mau et al. (2008) focused on preferences for HEVs and Fuel Cell Vehicles (FCVs) and manipulated market conditions in order to

The model

As we were interested in the dynamic interaction among variables of the system, and considering the diffusion methods commonly used to analyze the diffusion of AFVs, there were two main dynamic simulation approaches from which we could choose from, SD and Agent Based Model (ABM).

SD is a modelling approach that enhances learning about complex systems behaviour. Most of these complex behaviours arise from the interactions among the variables that are part of the system, i.e. feedbacks, and not

The Portuguese diffusion model

The Portuguese market was chosen as scope for the diffusion model developed in this study. The analysis of Portuguese market dynamics highlight why Portugal is an appealing context to address the diffusion of AFVs.

Targeting a 5% share of AFVs in 2020 (IEA, 2015), the Portuguese government has been implementing several incentive policies to favour the market penetration of AFVs (mainly BEVs). Purchase subsidies, exemption of purchase tax and circulation tax, and the development of charging

Robustness analysis of the transition preferences models

As mentioned earlier two DP models were computed in order to verify if the models' outputs were robust. Observing the transition of preferences over time (1-λt) of each model allows verifying that the transition in Model DP1 occurs linearly through the simulation period, while the transition in Model DP2 ends in 2046, when the value defined as attractive range is reached (Fig. 8). Fig. 9, Fig. 10 depict the evolution of the LDV fleet and of the AFVs market share for each DP model. The results

Conclusions

This paper aimed at a) incorporating dynamic preferences on the AFVs diffusion model and b) analyzing the effectiveness of subsidy policies adapted to dynamic preferences. As to the authors' best knowledge no other study incorporated dynamic preferences on diffusion models or simulated policies that were adapted accordingly, this study provides both methodological and empirical contributions to the literature. The methodological approach consisted in the incorporation of dynamic preferences in

Acknowledgements

This work is framed under the Energy for Sustainability Initiative of the University of Coimbra and supported by the Energy and Mobility for Sustainable Regions – EMSURE – Project (CENTRO-07-0224-FEDER-002004). The support from Portuguese Science and Technology Foundation (FCT) project UID/MULTI/00308/2013 and Doctoral Grant SFRH/BD/51639/2011 is also acknowledged.

Gabriela D. Oliveira received the Licentiate Degree in Management from the University of Coimbra in 2011. She is currently a PhD Candidate in Sustainable Energy Systems, under the MIT Portugal Program, at the University of Coimbra. Her research is about modelling consumer preferences for electric vehicles in the Portuguese market. Her research interests are multicriteria decision analysis, discrete choice experiments, system dynamics and transportation. She is with the INESC Coimbra and CeBER

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    Gabriela D. Oliveira received the Licentiate Degree in Management from the University of Coimbra in 2011. She is currently a PhD Candidate in Sustainable Energy Systems, under the MIT Portugal Program, at the University of Coimbra. Her research is about modelling consumer preferences for electric vehicles in the Portuguese market. Her research interests are multicriteria decision analysis, discrete choice experiments, system dynamics and transportation. She is with the INESC Coimbra and CeBER R&D Institutes.

    Richard Roth received his bachelor's degree in Materials Science and Engineering (1986) and a PhD degree in the same field (1992) from the Massachusetts Institute of Technology (MIT). He is currently the director of the Materials Systems Laboratory, a research group at MIT that studies the strategic implications of materials and materials processing choices. His research areas include materials criticality and availability, mining/materials supply and demand modeling, automotive lightweight materials cost and manufacturing modeling and technology learning.

    Luis C. Dias obtained a degree in Informatics Engineering from the School of Science and Technology at the University of Coimbra in 1992, a Ph.D. in Management by the University of Coimbra in 2001, and Habilitation in Decision Aiding Science in 2013 in the same university. He is currently Associate Professor and Vice-Dean for Research the Faculty of Economics, University of Coimbra (FEUC), where he has been teaching courses on decision analysis, operations research, informatics, and related areas. He held temporary invited positions at the University Paris-Dauphine and the University of Vienna. Luis is also a researcher at the CeBER and INESC Coimbra R&D centers, a member of the coordination board of U.Coimbra's Energy for Sustainability Initiative, and currently a Vice-President of APDIO, the Portuguese Operational Research Society. He is on the Editorial Board of the EURO Journal on Decision Processes and Omega. His research interests include multicriteria decision analysis, performance assessment, group decision and negotiation support, decision support systems, and applications in the areas of energy and environment.

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