Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and bayes estimates of a multinomial probit model

In this paper we use Bayes estimates of a multinomial probit model with fully exible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use statedpreference data on vehicle choice from a Germany-wide survey of potential lightduty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

This paper evaluates the market penetration of cars with alternative fuelpowertrain technologies in Italy under various scenarios. Seven cars on sale in 2013 are considered: the Ford Fiesta (diesel), the VW Polo (gasoline), the Fiat Punto Evo (bi-fuel \u2013 CNG), the Natural Power Alfa Romeo Mito (bi-fuel \u2013 LPG), the Toyota Yaris (hybrid \u2013 gasoline), the Peugeot iOn (BEV \u2013 owned battery), the Renault Zoe (BEV \u2013 leased battery). A Mixed Error Component Logit model is estimated based on data collected via a stated preference choice survey administered in 2013 in various Italian cities. The model's parameters are then used to build a Monte Carlo simulation model which allows evaluating, under different scenarios, the market penetration of the seven cars. The main findings are that (a) the subsidies enacted by the Italian government in favour of the low CO2 emitting cars appear to favour mostly the Ford Fiesta (diesel); (b) a three-fold increase in the BEVs range would not change their market share significantly (about 2%); and (c) only a combination of changes such as the introduction of a subsidy equal to \u20ac5000, the decrease of the purchase price for BEVs by \u20ac5000, the increase in the battery range, and the increase in the conventional fuel price would significantly increase the BEVs' market share, raising it to about 15%

Electric Vehicles Diffusion Forecasting Based on Consumer Preference

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 8. 이덕주.최근 몇 년 동안 석유가격의 변동과 탄소배출 등 환경 문제가 이슈화되면서 친환경 자동차에 대한 수요가 증가하고 있는 추세이다. 하지만 내연기관자동차에 대한 익숙함과 친환경자동차에 대한 사람들의 인식 부족으로 인해 친환경자동차에 대한 수요가 불명확한 상태이다. 따라서 소비자들의 친환경자동차에 대한 인식을 파악하기 위해 소비자들의 선호도를 측정하여 예측하는 방법이 거시적인 판매량 추이만을 통해 예측하는 방법보다 시장상황과 정책을 고려할 수 있어 보다 현실적인 결과를 얻을 수 있다. 본 논문에서는 자동차 선택 게임을 통해 각 요인이 소비자 선호도에 대한 기여도를 확인하고자 한다. 그리고 MNL(MultiNomial Logit) 모형을 이용하여 소비자 선호도를 정량적으로 측정하고, 각종 예상 시나리오 하에서 친환경자동차의 확산 추이를 알아보고자 한다. 주요어: 전기자동차, 확산예측, 소비자 선호도제 1 장 서론 1 1.1 연구 배경 및 목적 1 1.2 논문 구성 5 제 2 장 선행연구 6 2.1 친환경자동차 선택에 영향을 미치는 요인 6 2.2 친환경자동차 확산 예측 모형 8 제 3 장 전기자동차 확산 예측 절차 9 3.1 설문조사 9 3.2 다항로짓모형을 이용한 효용함수 추정 13 3.3 전기자동차 확산 예측 15 제 4 장 분석 결과 19 4.1 효용함수 추정 결과 19 4.2 전기자동차 확산 예측 결과 23 4.2.1 시나리오 1 23 4.2.2 시나리오 2 24 4.2.3 시나리오 3 25 4.2.4 시나리오 4 27 4.2.5 시나리오 5 29 4.2.6 시나리오 6 31 4.2.7 시나리오 7 33 제 5 장 결론 36 5.1 결론 36 5.2 향후 연구 계획 38 참고 문헌 38 Abstract 43Maste

Methods for forecasting the market penetration of electric drivetrains in the passenger car market

Current car technologies will not solve upcoming challenges of mitigating greenhouse gas emissions in road transport. Projections of the market penetration by alternative drive train technologies are controversial regarding both forecast market shares and applied scientific methods. Accepting this latter challenge, we provide a (so far missing) overview of methods applied in this field and give some recommendations for further work. Our focus is to classify the applied methods into a convenient pattern and to analyse models from the recent scientific literature which consider the electrification of light-duty vehicles. We differentiate the following bottom-up approaches: Econometric models with disaggregated data (such as discrete choice), and agent-based simulation models. The group of top-down models are subdivided into econometric models with aggregated data (e.g. vehicle stock data), system dynamics, as well as integrated assessment models with general equilibrium models. It becomes obvious that some methods have a stronger methodological background whereas others require comprehensive data sets or can be combined more flexibly with other methods. Even though there is no dominant method, we can identify a trend in the literature towards data-driven hybrid approaches, which considers micro and macro aspects influencing the market penetration of electric vehicles

Renewable hydrogen supply chains: A planning matrix and an agenda for future research

Worldwide, energy systems are experiencing a transition to more sustainable systems. According to the Hydrogen Roadmap Europe (FCH EU, 2019), hydrogen will play an important role in future energy systems due to its ability to support sustainability goals and will account for approximately 13% of the total energy mix in the coming future. Correct hydrogen supply chain (HSC) planning is therefore vital to enable a sustainable transition, in particular when hydrogen is produced by water electrolysis using electricity from renewable sources (renewable hydrogen). However, due to the operational characteristics of the renewable HSC, its planning is complicated. Renewable hydrogen supply can be diverse: Hydrogen can be produced de-centrally with renewables, such as wind and solar energy, or centrally by using electricity generated from a hydro power plant with a large volume. Similarly, demand for hydrogen can also be diverse, with many new applications, such as fuels for fuel cell electrical vehicles and electricity generation, feedstocks in industrial processes, and heating for buildings. The HSC consists of various stages (production, storage, distribution, and applications) in different forms, with strong interdependencies, which further increase HSC complexity. Finally, planning of an HSC depends on the status of hydrogen adoption and market development, and on how mature technologies are, and both factors are characterised by high uncertainties. Directly adapting the traditional approaches of supply chain (SC) planning for HSCs is insufficient. Therefore, in this study we develop a planning matrix with related planning tasks, leveraging a systematic literature review to cope with the characteristics of HSCs. We focus only on renewable hydrogen due to its relevance to the future low-carbon economy. Furthermore, we outline an agenda for future research, from the supply chain management perspective, in order to support renewable HSC development, considering the different phases of renewable HSCs adoption and market development

Modeling purchases of new cars: an analysis of the 2014 French market

This paper analyses and compares different policy scenarios as well as discusses price elasticities and willingness to pay and to accept using revealed preference data from the French new-car market in 2014 by means of a cross-nested logit (CNL) model. We focus particularly on electric and hybrid vehicles. We use interactions between the cost (both fixed and running costs) and the household income in order to analyze the sensitivity towards different policy scenarios per income level. Results show that the willingness to pay and to accept obtained in our study are consistent with the real market conditions. We also find that the most effective scenario in order to increase the market shares of new sold electric vehicles is that of a major technological advance such as a decrease in price due to cheaper manufacturing costs and an increase in driving range, rather than a policy-based scenario. Also, the market segment that has more potential to increase the market shares of electric vehicle purchase is the middle-income level. In the paper, we discuss how to overcome the difficulties of working with revealed preference data, and propose a new method to impute the attributes of the unchosen alternatives, based on the empirical distributions observed in the data

Deciphering the Factors Associated With Adoption of Alternative Fuel Vehicles in California: An Investigation of Latent Attitudes, Socio-Demographics, and Neighborhood Effects

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as:Xiatian Iogansen, Kailai Wang, David Bunch, Grant Matson, Giovanni Circella, Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects, Transportation Research Part A: Policy and Practice, Volume 168, 2023, 103535, ISSN 0965-8564, https://doi.org/10.1016/j.tra.2022.10.012.Promoting the use of alternative fuel vehicles (AFVs) has become a long-term transportation strategy in California, which can bring a broad range of social, economic, and environmental benefits. Based on a sample of 3260 California residents from the 2018 California Panel Survey, this study explores the impacts of latent attitudes, socio-demographic characteristics, and neighborhood effects on consumers\u2019 current vehicle fuel type choice and their interest in purchasing or leasing an AFV in the future

UNDERSTANDING AND MODELLING TIME USE, WELL BEING AND DYNAMICS IN ACTIVITY-TRAVEL BEHAVIOR: A CHOICE BASED APPROACH

Understanding the determinants of activity and travel related choices is critical for policy-makers, planners and engineers who are in charge of the management and design of large scale transportation systems. These systems, and their externalities, are interwoven with human actions and communities’ evolution. Traditionally, individual decision-making and travel behaviour studies are based on random utility models (RUM) and discrete choice analysis. To extend the ability of modellers to represent and forecast complex travel behaviour, this dissertation expands existing models to accommodate the influence of variables other than the traditional socio-demographics or level of service variables. In this thesis, technology innovations, psychological factors, and perceptions of future uncertainty are integrated into the classical RUMs and their effects on activity-travel decision making are investigated. Technology innovations, such as telecommunication, online communities and entertainment, release individual’s time and space constraints. They also modify people’s activity and travel choices. An integrated discrete-continuous RUM is proposed to study individuals’ participation in leisure activities, which is an important component of activity scheduling analysis and tour/trip formation. Leisure alternatives considered include: computer/internet related activity, in-home activity, and out-of-home activity. Compared to previous discrete-continuous models, interdependence among activities and the related time usage is explored using a modelling structure that accommodate full correlation among decision variables of different types. Standard random utility models are extended by including attitudes and perceptions as latent variables; these constructs are expected to enhance the behavioural representation of the choice process. A simultaneous structural model is proposed to represent the mutual effects existing between psychological factors and activity choices. Biases due to endogeneity in psychological factors and activity choices are taken into consideration in the model. To further extend the behavioural realism of our model, this thesis proposes a new simultaneous equation model formulation that links psychological indicators to activity participation and time use decisions. Unlike previous studies, the proposed method allows the psychological factors to be correlated with time use decisions and serve as an attribute in time use choice model. A new iterative simulated maximization estimation method is also proposed to accommodate possible endogeneity bias in the model system. A simulation experiment shows that the estimation method produces consistent and unbiased estimation results. Moreover, a real case study is also implemented in the context of participation in leisure activities, linking emotions, activity involvement and time use. After exploring individual’s decisions on activity and time use choices, a dynamic discrete choice model framework is proposed to accommodate stochasticity in individual behaviour over time. Following previous studies, activity patterns are decomposed into tour and stop sequences. Accordingly, a tour choice model and a stop choice model are jointly formulated under a unified framework with a hierarchical structure where stop choices are assumed to be conditional on tour choices. The results indicate that individuals are sensitive to current and future changes in travel and activity characteristics and that a dynamic formulation better represents multi-day travel behaviour

Understanding Australian consumer behaviour towards electric vehicles

This study provided unique insight into the Australian consumer’s adoption of electric vehicles. The research applied a novel framework, jointly exploring psychological constructs, such as attitudes, in conjunction with vehicle attributes and sociodemographic characteristics. The findings highlight the central role of psychological constructs in decision making and electric vehicle adoption