The Impact of Ride-Hailing Services on Private Car Use in Urban Areas: An Examination in Chinese Cities

Zhong, Jinhong; Lin, Yan; Yang, Siqi

doi:10.1155/2020/8831674

Cited by 19 publications

(11 citation statements)

References 69 publications

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“…e predicted carbon emissions are the prediction results of the neural network, and carbon emissions per unit mileage are the carbon emissions per unit kilometer of a private car. e emission reduction subsidy is calculated by formula (1).…”

Section: Comparison Of Simulation Resultsmentioning

confidence: 99%

“…Driving mileage, driving time, and driving fuel consumption are taken as the predictive variables of the neural network. By adjusting the number of neurons in the hidden layer, the error between the BP-AdaBoost neural network model and the BP-AdaBoost neural network model established in this study is given in Table 1 [1].…”

Section: Error Comparison Between the Two Modelsmentioning

confidence: 99%

“…Although China has introduced the carbontrading policy, the low-carbon policy mix of road traffic carbon emission reduction has not been put into practice. By March 2021, the number of motor vehicles in China have reached 378 million [1], and its carbon emissions have become the third largest source of carbon emissions in China, which poses a huge challenge to achieve carbon peak and carbon neutrality. is study combines private car data with low-carbon policy mix innovation to study effective emission reduction measures to promote sustainable development of smart city.…”

Section: Introductionmentioning

confidence: 99%

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Benefit Analysis of Low-Carbon Policy Mix Innovation Based on Consumer Perspective in Smart City

Chen

Ngabo

2021

Scientific Programming

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In the construction of smart city, the carbon emission reduction problem of road traffic needs to be solved urgently. It is of great significance to introduce reasonable low-carbon policies. Based on urban private cars trajectory data, this study, respectively, establishes the genetic algorithm-back propagation neural network model (GA-BP) and back propagation-adaptive boosting algorithm neural network model (BP-AdaBoost) to predict the carbon emissions of private cars. By comparing the two neural network models, the GA-BP neural network model has better prediction results. Next, this study establishes the cost-benefit model for consumers and compares consumers’ participation willingness, emission reduction effect, and social benefits of consumers from the perspective of six kinds of low-carbon policies. The results show that the overall effect of the low-carbon policy mix of free quota is better than that of paid quota. In addition, different low-carbon policy mixes innovations have different policy implementation effects under different indicators. Overall, the low-carbon policy mix of carbon trading and emission reduction subsidy is better in the short term, and the low-carbon policy mix of carbon tax and emission reduction subsidy is better in the long term.

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Section: Comparison Of Simulation Resultsmentioning

confidence: 99%

Section: Error Comparison Between the Two Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Benefit Analysis of Low-Carbon Policy Mix Innovation Based on Consumer Perspective in Smart City

Chen

Ngabo

2021

Scientific Programming

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“…A different study (Zhong et al, 2020) analyzes the impact of ride-hailing services on the use of private cars with data from 109 cities in China from 2010 to 2016. It shows that the use of private cars initially reduced substantially, then weakened over time.…”

Section: Ride-hailing Apps In Developing Countriesmentioning

confidence: 99%

Environmental Challenges of Digital Transformation in Developing Countries

Hicks¹

2021

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This rapid review synthesises the literature on the environmental risks of four different aspects of digital transformation in developing countries: ICT adoption, digitally-enabled energy efficiency, ride-hailing apps, and big data use. The overall message which runs throughout the diverse literatures and results is that it is not digital technologies that create environmental risks or opportunities, but how they are used. Efficiency gains derived from digital transformation may yet lead developing countries down existing unsustainable development paths if not accompanied by careful, context-dependent policy. For policy-makers seeking to mitigate environmental risks, this means putting the context of digital use at the centre of analysis rather than the technologies themselves. However, the research literature covers more specific aspects of digital transformation. In practice, this report defines digital transformation as: ICT adoption, digitally-enabled energy efficiency, ride-hailing apps, and big data use. These topics were chosen after an initial scoping review of available literature, and because they exemplify a range of the different types of potential digital effects. The literature on the environmental risks of digital transformation is huge and conflicting. This is problematic because it could be easy to cherry pick preferred research results. Several studies noted that there is less research on developing countries than developed countries, but the evidence base is still large. As an evidence review, this report focused on the academic literature, but there is also a large grey literature. Some of the literature has a gender aspect, not covered in this report.

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“…It collects drivers offering ride services to the wider public based on coordination through a platform for centralized dispatch [3]. The popularity of online car-hailing service relies not only on advanced technology, but also on a lower price [4], higher flexibility [5], and environment friendly [6][7]. Therefore, the emergence of online carhailing service presents a tremendous disadvantage to traditional taxi service.…”

Section: Introductionmentioning

confidence: 99%

Comprehending the Pricing Decision of Online Car-Hailing Services in China: Price Regulation Vs Entry Limitation

Yang¹,

Zhao²,

Wang³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Online car-hailing service has had an exponential growth in recent years, and poses a substantial threat to taxi service. Yet how to regulate online car-hailing service has not been adequately studied. Based on the regulation of tradition taxi service, price regulation and entry limitation are used to regulate online car-hailing service in this paper. Moreover, we consider two types of online car-hailing service, i.e., high-end service(e.g. UberBlack) and low-end service (e.g., UberX), according to the perceived service level. Then, the optimal platform’s price is formulated. The result shows that the price regulation are likely to increase the optimal platform price, depending on the service type and the taxi price. When the platform offers low-end service and the taxi price is low, the optimal platform price does not change. In contrast, the entry limitation reduces the optimal platform’s price when it achieves the regulation target.

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The Impact of Ride-Hailing Services on Private Car Use in Urban Areas: An Examination in Chinese Cities

Cited by 19 publications

References 69 publications

Benefit Analysis of Low-Carbon Policy Mix Innovation Based on Consumer Perspective in Smart City

Benefit Analysis of Low-Carbon Policy Mix Innovation Based on Consumer Perspective in Smart City

Environmental Challenges of Digital Transformation in Developing Countries

Comprehending the Pricing Decision of Online Car-Hailing Services in China: Price Regulation Vs Entry Limitation

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