Forecasting the Reduction in Urban Air Pollution by Expansion of Market Shares of Eco-Friendly Vehicles: A Focus on Seoul, Korea

Jo, Hanghun; Kim, Seong-A; Kim, Heungsoon

doi:10.3390/ijerph192215314

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…For instance, artificial intelligence algorithms can help predict vehicle carbon emissions, as seen in a novel estimation method based on multifactor macro-fundamental diagrams for full sample traffic volume and overall road network emissions, to achieve road network carbon emission estimations [ 16 ]. Another model focuses on the achievement of carbon neutrality, targeting for the replacement of medium and large passenger cars with pure electric vehicles in Seoul [ 17 ]. Through the analysis of individuals’ choices of pure EVs, Li et al find that driving cost, charging efficiency, and convenience of charging stations are the main influencing factors of NEV consumption [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Dual Carbon Goal-Based Quadrilateral Evolutionary Game: Study on the New Energy Vehicle Industry in China

Liu

et al. 2023

IJERPH

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In an effort to tackle climate change, the “Dual Carbon” target raised by the Chinese government aims to reach peak carbon dioxide emissions by 2030 and to achieve carbon neutrality by 2060. Accordingly, policy incentives have accelerated the new energy vehicle (NEV) sector. Whilst previous studies have focused on the bilateral game between governments and manufacturers, NEV development has witnessed interaction among multiple players. In this paper, we construct a quadrilateral evolutionary game model, considering the impact of government policies, manufacturers’ R&D investments, dealers’ support, and consumer choice on the evolutionary stabilization strategy (ESS) in the context of China. The results show that: (1) in the absence of government incentives, there is no motivation for manufacturers, dealers and consumers to consider the development of NEVs; (2) government incentives affect manufacturers and consumers on the evolutionary paths in the short term. In the long term, benefit- and utility-based limited rationality has a dominant role in the ESS. This study contributes to the understanding of the multilateral dynamics of NEV innovation and provides important implications to practitioners and policy makers.

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Section: Literature Reviewmentioning

confidence: 99%

Dual Carbon Goal-Based Quadrilateral Evolutionary Game: Study on the New Energy Vehicle Industry in China

Liu

et al. 2023

IJERPH

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“…The use of fossil fuels, such as petroleum, has been identified as the main cause of fine dust in the country. According to the 2019 petroleum consumption trends compiled by the Korea National Oil Corporation, the total consumption of petroleum products in the country has increased by 20% compared with 10 years ago, particularly due to the increase in the number of vehicle registrations [9,10]. This is because Korea built cities and roads from the perspective of vehicles moving in the city, rather than of people living in the city, for rapid economic growth [11].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of Ecosystem Services Provided by Street Trees in Seoul for the Suggestion of Social Equity

Choi,

Machado,

Kim

2024

Land

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Cities must overcome their limitations on quantitative growth by pursuing sustainable development. Road-centered development leads to social problems, including inequality, during urban expansion. This study focuses on quantifying the ecosystem services provided by street trees in the Gangnam and Gangbuk Districts of Seoul, South Korea. This research utilized public data and field surveys conducted between April and August 2023, which is the best time to grow coniferous and deciduous trees. The results helped quantify the improvement in the air quality and ecological economic value from the perspective of plant species structure and carbon storage. The street trees in Gangnam stored 3691.61 t (metric tons) and sequestered 359.45 t of carbon; removed 2.28 t of air pollutants; and reduced 3977.46 m3 of rainwater runoff annually. In contrast, the street trees in Gangbuk stored 831.61 t and sequestered 74.97 t of carbon; removed 0.4 t of air pollutants; and annually reduced rainwater runoff by 1491.74 m3. By quantifying the ecosystem services of street trees, this study showed a gap in the supply of ecosystem services, which are relatively poor in economically poor areas, thus contributing to a more holistic understanding of urban growth and advocating for inclusive and socially equitable development strategies and management policies in Seoul.

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Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization

Nguyen,

Pham,

et al. 2024

J Big Data

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Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels.

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Forecasting the Reduction in Urban Air Pollution by Expansion of Market Shares of Eco-Friendly Vehicles: A Focus on Seoul, Korea

Cited by 4 publications

References 44 publications

Dual Carbon Goal-Based Quadrilateral Evolutionary Game: Study on the New Energy Vehicle Industry in China

Dual Carbon Goal-Based Quadrilateral Evolutionary Game: Study on the New Energy Vehicle Industry in China

A Comparative Evaluation of Ecosystem Services Provided by Street Trees in Seoul for the Suggestion of Social Equity

Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization

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