Falling short in 2030: Simulating battery-electric vehicle adoption behaviour in the Netherlands

Paradies, Geerte L.; Usmani, O.A.; Lamboo, Sam; Brink, R.W. van den

doi:10.1016/j.erss.2023.102968

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…These findings are comparable to those of Setiawan et al [36], who discovered the negative influence of ELV taxation on ELV adoption amongst Indonesian consumers. In parallel, Paradies et al [37] also determined the high price of ELVs as a barrier to their adoption; however, they further observed that this barrier would be less intensive by 2030. Given these results, the HIFs were grouped into three categories: (i) drivers, (ii) barriers, and (iii) neutral factors.…”

Section: Structural Model Results and Discussionmentioning

confidence: 99%

“…They discovered that high taxation on ELVs impeded their acceptance. A study by Paradies et al [37] employed survey-based data from prospective consumers of ELVs in the Netherlands to inspect the influence of pricing and driving range on future ELV acceptance. They found that the pricing factor was a barrier to ELV acceptance among the sampled data.…”

Section: Capital Cost Of Elvs (Ccelv)mentioning

confidence: 99%

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Heterogeneous Factors Influencing Electric Vehicle Acceptance: Application of Structural Equation Modeling

Guo

Huang²,

Chen³

et al. 2023

WEVJ

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Since electric vehicle (ELV) deployment can contribute to overall renewable energy sources, exploration of the heterogeneous influence factors (HIFs) affecting the willingness to accept ELVs can assist in the realization of sustainable development goals, particularly universal access to affordable energy for all. In this research, we explored the HIFs that influence the willingness of individuals to accept ELVs (WAELV) within an integrated decision-making (IDM) framework. We established the IDM conceptual framework through the incorporation of HIFs, notably including the environmental and health benefits of ELVs, knowledge about innovation, and the benefits regarding the built environment and creating a comprehensive structure. We analyzed data gathered through questionnaires from urban and peri-urban areas of the Shandong province (China) by employing the partial least square structural equation modeling technique, which is an appropriate tool for analyzing data measured on a Likert scale. The key findings were as follows. Firstly, the capital cost of ELVs was found to be a significant barrier to the WAELV of individuals. Secondly, among other factors, the societal aspect of ELVs and the environmental awareness aspect were drivers of the WAELV of individuals across all the data samples. However, benefits for the built environment, knowledge about innovation, and the environmental and health benefits of ELVs only positively drove the WAELV of individuals in the urban setting and for the overall sample. Thirdly, these three HIFs were identified as neutral factors in the peri-urban areas. Thus, a clear disparity was detected between the urban and peri-urban areas in terms of factors influencing the WAELV of individuals. Finally, the social aspect of ELVs was revealed as the strongest driver, while benefits for the built environment turned out to be the weakest factor. Based on these findings, some crucial policies are here extracted.

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Section: Structural Model Results and Discussionmentioning

confidence: 99%

Section: Capital Cost Of Elvs (Ccelv)mentioning

confidence: 99%

Heterogeneous Factors Influencing Electric Vehicle Acceptance: Application of Structural Equation Modeling

Guo

Huang²,

Chen³

et al. 2023

WEVJ

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“…RP refers to an individual's willingness to take risks. Risk aversion to new technologies has been used to model the uptake of EVs [28] , [29] , [30] , [31] . Like EC, the results are split in terms of finding a significant effect [31] or not [29] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Survey data on the socio-economic characteristics and electric vehicle perceptions of paratransit owners and drivers in and around the Cape Town Metropole of South Africa

Hull,

Giliomee,

Visser

et al. 2024

Data in Brief

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“…Yang et al (2023) investigated the impact of socioeconomic drivers and climatic conditions on EV adoption in Norway. Paradies et al (2023) identified routine purchasing behavior and social factors as barriers to widespread EV adoption in the Netherlands. However, these studies have predominantly focused on predicting EV sales within a single country or region.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model for Electric Vehicle Sales Using Machine Learning Approaches

Yeh,

Wang

2023

Journal of Global Information Management

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The electric vehicle (EV) market is booming, but EV market trends vary by region. This study draws on the environmental, economic, and human development factors of 31 countries to predict sales of EVs. Based on machine learning (ML) algorithms and the PLS method, the authors constructed an EV sales performance prediction model and carried out the experiments. The experimental results demonstrate that ML algorithms can effectively achieve the desired accuracy and predictive performance levels. At the same time, this study investigates the relationship between quality training indicators and EV sales. CO2 emissions, PM2.5, consumer price index (CPI), renewable energy, and life expectancy are found to be significantly positive related to EV sales. The proposed model can be used globally by governments as a decision support tool to impose policies encouraging the adoption of EVs and develop sustainable strategies.

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Falling short in 2030: Simulating battery-electric vehicle adoption behaviour in the Netherlands

Cited by 15 publications

References 36 publications

Heterogeneous Factors Influencing Electric Vehicle Acceptance: Application of Structural Equation Modeling

Heterogeneous Factors Influencing Electric Vehicle Acceptance: Application of Structural Equation Modeling

Survey data on the socio-economic characteristics and electric vehicle perceptions of paratransit owners and drivers in and around the Cape Town Metropole of South Africa

A Prediction Model for Electric Vehicle Sales Using Machine Learning Approaches

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