Does air pollution stimulate electric vehicle sales? Empirical evidence from twenty major cities in China

This article has taken up an issue concerning the influence of the implementation of electromobility assumptions on reduction of air pollution in cities in terms of sustainable urban transport systems. The essential nature of the subject is confirmed by the following figures: transport is responsible for almost 25% of greenhouse gas emissions in Europe and is the main cause of air pollution in cities, almost 90% of city dwellers in the EU are exposed to levels of air pollutants deemed harmful by the WHO, and approximately 95% of vehicles on European roads still use fossil fuels. Therefore, the implemented transport policy, both in the international and domestic aspect of particular countries, is facing the need to significantly reduce the negative influence of transport on people and the environment. Electromobility has become one of the concepts that makes it possible to achieve this goal. Its main objective is to reduce emissions of harmful substances into the environment by deploying electric vehicles (EVs). Research conducted by the authors showed that public transport, apart from the obvious effect of decreased number of private vehicles and decreased congestion, can contribute to reduced air pollutant emissions and become a significant driver for the implementation of electromobility in cities. However, the achievement of this goal depends on taking appropriate actions not only in the transport branch but also in other sectors of economy. The following research methods were applied in the article: literature analysis, documentary method, case study, and mathematical methods. The research area was the city of Szczecin, Poland.

https://www.mdpi.com/2071-1050/12/3/1052/pdf

Environmental Effects of Electromobility in a Sustainable Urban Public Transport

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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Leo Kim

Papers

Korea's social dynamics towards power supply and air pollution caused by electric vehicle diffusion