Green Travel: Exploring the Characteristics and Behavior Transformation of Urban Residents in China

Han, Je-Chin; Appolloni, Andrea; Wang, Yunqi

doi:10.3390/su9061043

Cited by 28 publications

(17 citation statements)

References 36 publications

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“…In the policies, regulations are made in an attempt to reduce vehicle operation cost and ensure environmental friendliness. To achieve this, policies, programs, and other regulations are imposed in many countries with a view to curtailing energy consumption and, perhaps, carbon emission [65,66]. Most governments nowadays invest substantially in the transportation sector.…”

Section: Safety and Driving Behaviourmentioning

confidence: 99%

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Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

et al. 2018

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Climate change is receiving increasing attention in recent years. The transportation sector contributes substantially to increased fuel consumption, greenhouse gas (GHG) emissions, and poor air quality, which imposes a serious respiratory health hazard. Road transport has made a significant contribution to this effect. Consequently, many countries have attempted to mitigate climate change using various strategies. This study analysed and compared the number of policies and other approaches necessary to achieve reduced fuel consumption and carbon emission. Frequency aggregation indicates that the mitigation policies associated with driving behaviours adopted to curtail this consumption and decrease hazardous emissions, as well as a safety enhancement. Furthermore, car-sharing/carpooling was the least investigated approach to establish its influence on mitigation of climate change. Additionally, the influence of such driving behaviours as acceleration/deceleration and the compliance to speed limits on each approach was discussed. Other driving behaviours, such as gear shifting, compliance to traffic laws, choice of route, and idling and braking style, were also discussed. Likewise, the influence of aggression, anxiety, and motivation on driving behaviour of motorists was highlighted. The research determined that driving behaviours can lead to new adaptive driving behaviours and, thus, cause a significant decrease of vehicle fuel consumption and CO 2 emissions.

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Section: Safety and Driving Behaviourmentioning

confidence: 99%

“…The major challenge is the energy source used for these types of vehicles [125]. In this regard, the design of energy-efficient vehicles is encouraged by some governments to reduce fuel consumption and, perhaps, gas emissions [65]. This reduces the overdependence on oil markets.…”

Section: Changing Vehiclementioning

confidence: 99%

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

et al. 2018

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“…In this study, Q-methodology is applied to categorize experts' perceptions of urban green spaces and PM. Q-methodology was developed by psychologist William Stephenson in 1935 and is generally used as a technique to explore the subjective types of perception of an issue or object [36,37]. It is a way of quantifying qualitative content by applying statistical concepts; it is generally introduced to understand the process through which a phenomenon occurs [38].…”

Section: Q-methodologymentioning

confidence: 99%

“…Therefore, a total of 57 expert opinions were collected and analyzed in this study. P-sets should be comprised of different and well-formed opinions that cover a wide variety of views and represent the breadth of opinions in the target population being studied [36,44]. Therefore, here, the P-sample consists of 11 officials from government and public institutions, 13 researchers from university and research institutes, and 33 workers from landscape architecture design and construction companies ( Table 2).…”

Section: Data Collectionmentioning

confidence: 99%

Experts’ Perceptions on the Particulate Matter Reduction Effects of Green Open Space

Kim

Han²,

Lee

et al. 2019

Sustainability

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With recent declines in air quality, the significance of urban green spaces and their ecological functions have rapidly increased, especially with regard to the reduction of particulate matter. Various investigations regarding particle reduction in urban green spaces have been conducted; however, specific guidelines to establish empirical data for green spaces and to inform related policies are still lacking. Thus, this study aims to categorize experts’ perceptions of green spaces through Q-methodology and to identify ways to form a consensus, establish policies in the design and construction process, ultimately aiming to enhance particle reduction effects in urban green spaces. As a result, experts’ perceptions were classified into three categories: ‘active support,’ ‘skeptical,’ and ‘passive support’ groups. Experts’ opinions on the particle reduction effects of urban green areas are overarchingly agreed upon; however, the priorities involved and methods used in augmenting green space integration require further analysis and mediation. Additionally, further empirical evidence should be accumulated on the particulate matter reduction effects of urban green areas, including the quantification of particle concentration reduction in urban green spaces and considerations for policy establishment in design and construction.

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“…Developed economies are more focused on the rather elitist aspects of the SDGs such as climate action (SDG 13) [6,7], life below water (SDG 14), life on land (SDG 15), renewable energy [7], efficient waste disposal systems [8] and urban greening [9]. Conversely, developing countries are faced with overcoming issues articulated in SDGs 1-6 (poverty, hunger, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-Based Advisory Framework for Attainment of Sustainable Development Goals 1-6

et al. 2020

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Research in sustainable development, program design and monitoring, and evaluation requires data analytics for the Sustainable Developments Goals (SDGs) not to suffer the same fate as the Millennium Development Goals (MDGs). The MDGs were poorly implemented, particularly in developing countries. In the SDGs dispensation, there is a huge amount of development-related data that needs to be harnessed using predictive analytics models such as deep neural networks for timely and unbiased information. The SDGs aim at improving the lives of citizens globally. However, the first six SDGs (SDGs 1-6) are more relevant to developing economies than developed economies. This is because low-resourced countries are still battling with extreme poverty and unacceptable levels of illiteracy occasioned by corruption and poor leadership. Inclusive innovation is a philosophy of SDGs as no one should be left behind in the global economy. The focus of this study is the implementation of SDGs 1-6 in less developed countries. Given their peculiar socio-economic challenges, we proposed a design for a low-budget deep neural network-based sustainable development goals 1-6 (DNNSDGs 1-6) system. The aim is to empower actors implementing SDGs in developing countries with data-based information for robust decision making.

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Green Travel: Exploring the Characteristics and Behavior Transformation of Urban Residents in China

Cited by 28 publications

References 36 publications

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

Experts’ Perceptions on the Particulate Matter Reduction Effects of Green Open Space

A Deep Neural Network-Based Advisory Framework for Attainment of Sustainable Development Goals 1-6

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