The use of renewable energy resources, such as solar, wind, and biomass will not diminish their availability. Sunlight being a constant source of energy is used to meet the ever-increasing energy need. This review discusses the world's energy needs, renewable energy technologies for domestic use, and highlights public opinions on renewable energy. A systematic review of the literature was conducted from 2009 to 2018. During this process, more than 300 articles were classified and 42 papers were filtered for critical review. The literature analysis showed that despite serious efforts at all levels to reduce reliance on fossil fuels by promoting renewable energy as its alternative, fossil fuels continue to contribute 73.5% to the worldwide electricity production in 2017. Conversely, renewable sources contributed only 26.5%. Furthermore, this study highlights that the lack of public awareness is a major barrier to the acceptance of renewable energy technologies. The results of this study show that worldwide energy crises can be managed by integrating renewable energy sources in the power generation. Moreover, in order to facilitate the development of renewable energy technologies, this systematic review has highlighted the importance of public opinion and performed a real-time analysis of public tweets. This example of tweet analysis is a relatively novel initiative in a review study that will seek to direct the attention of future researchers and policymakers toward public opinion and recommend the implications to both academia and industries.

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Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions

Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach

The study of respondent heterogeneity has been one of the main areas of research in the field of choice modelling in recent years. The emphasis has been on variations across respondents in the parameters used in the utility function while maintaining the assumption that the actual utility specification is generic across respondents. Recent work has moved on from this by allowing for differences in the utility specification across respondents in terms of inclusion or otherwise of specific attributes, in the context of work looking at heterogeneous information processing strategies. The underlying assumption that all respondents employ the same choice paradigm remains, despite evidence in the literature that different paradigms work differently well on given datasets. In this paper, we go one step further by presenting a latent class framework in which the model at the sample population level is a mixture of different individual models. We present applications on three different datasets, showing mixtures between "standard" random utility maximisation models and lexicography based models, models with multiple reference points, elimination by aspects models and random regret minimisation models. In each of the case studies, the behavioural mixing model obtains significant gains in fit over the base structure where all respondents are hypothesised to use the same rule. The findings offer important further insights into the behavioural patterns of respondents. There is also evidence that what is retrieved as taste heterogeneity in standard models may in fact be heterogeneity in decision rules.

Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies

The spread of electric vehicles (EVs) and their increasing demand for electricity has placed a greater burden on electricity generation and the power grid. In particular, the problem of whether to expand the electricity power stations and distribution facilities due to the construction of EV charging stations is emerging as an immediate issue. To effectively meet the demand for additional electricity while ensuring the stability of the power grid, there is a need to accurately predict the charging demands for EVs. Therefore, this study estimates the changes in electricity charging demand based on consumer preferences for EVs, charging time of day, and types of electric vehicle supply equipment (EVSE) and elucidates the matters to be considered for constructing EV infrastructure. The results show that consumers mainly preferred charging during the evening. However, when we considered different types of EVSEs (public and private) in the analysis, people preferred to charge at public EVSEs during the day. During peak load time, people tended to prefer charging using fast public EVSEs, which shows that consumers considered the tradeoffs between the full charge time and the price for charging. Based on these findings, this study provides key political implications for policy makers to consider in taking preemptive measures to adjust the electricity supply infrastructure.

Forecasting electricity demand of electric vehicles by analyzing consumers’ charging patterns

The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties

Do people really want renewable energy? Who wants renewable energy?: Discrete choice model of reference-dependent preference in South Korea

Dynamic analysis of well-to-wheel electric and hydrogen vehicles greenhouse gas emissions: Focusing on consumer preferences and power mix changes in South Korea

Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model

Junghun Kim

Papers

The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties

Do people really want renewable energy? Who wants renewable energy?: Discrete choice model of reference-dependent preference in South Korea

Dynamic analysis of well-to-wheel electric and hydrogen vehicles greenhouse gas emissions: Focusing on consumer preferences and power mix changes in South Korea

Can mobile and biometric payments replace cards in the Korean offline payments market? Consumer preference analysis for payment systems using a discrete choice model

Smartphone preferences and brand loyalty: A discrete choice model reflecting the reference point and peer effect