Spatial-modal scenarios of greenhouse gas emissions from commuting in Hong Kong

Chow, Alice S.Y.

doi:10.1016/j.jtrangeo.2016.06.001

Cited by 18 publications

(8 citation statements)

References 21 publications

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“…Finally, we should not forget the issue of the environmental impact of mode choice since the transport sector is the second largest source of greenhouse gas emissions (after energy production) [28,29,30]. Our finding about the predominance of car-only users (over public transport users) indicates a shortfall in the development of a sustainable transport system in Croatia.…”

Section: Resultsmentioning

confidence: 79%

Analysis of Socio-spatial Differentiation in Transport Mode Choice Preferences

Miletić

Gašparović

Carić³

2017

PROMET

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Section: Resultsmentioning

confidence: 79%

Analysis of Socio-spatial Differentiation in Transport Mode Choice Preferences

Miletić

Gašparović

Carić³

2017

PROMET

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“…As demonstrated elsewhere (Schwanen, 2016a), climate change is most prominently addressed as a concern in geographical research on urban passenger mobility, and a review of recent publications in Journal of Transport Geography confirms this (see e.g. Chow, 2016;Zhao et al, 2017;Keskisaaria et al, 2017;Conti, 2018). There continues to be only limited interest in climate change in Transport Geography work on freight, on aviation and maritime transport.…”

Section: Unevenness and Separationmentioning

confidence: 81%

Transport geography, climate change and space: opportunity for new thinking

Schwanen

2019

Journal of Transport Geography

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This paper reflects on how Transport Geography has examined the relationships between climate change, transport and space. It identifies four tendencies in engagements so faruneven attention; separation between climate change mitigation and adaptation; continuity in theory, method and research praxis; and over-reliance on 'Western' thinking and worldviews. It develops an agenda for further research that emphasises the importance of considering all modes and forms of transport, moving beyond the mitigation and adaptation distinction, and the socio-political nature of the relationships between climate change, transport and space. It also argues that research praxis has to be revised if those relationships are to be understood adequately, suggesting that critical reflexivity on concepts is a useful step in this direction. The paper then introduces and illustrates an approach to the (re)invention of concepts about the relationships between transport, climate change and space. It concludes by urging transport geographers to work with concepts co-produced with a much wider range of constituencies than usually considered and to develop new methods for tracking the events created by new concepts.

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“…These indicators influence residents' travel demand patterns as well as their travel mode, route selection, and other behavioral choices, thereby impacting travel emissions. For example, a reasonable layout between commercial, office, and residential areas can reduce commuting distances and times, thereby lowering long-distance travel demand and emissions [47]. Furthermore, a well-designed layout of public transportation infrastructure may attract people to choose public transit options, thereby reducing emissions from private vehicles [48].…”

Section: Factors Affecting Travel Emissionsmentioning

confidence: 99%

Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study

Gao,

Zhao,

Zeng

et al. 2024

Sustainability

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The rise of ride-hailing services presents innovative solutions for curbing urban carbon emissions, yet poses challenges such as fostering fair competition and integrating with public transit. Analyzing the factors influencing ride-hailing emissions is crucial for understanding their relationship with other travel modes and devising policies aimed at steering individuals towards more environmentally sustainable travel options. Therefore, this study delves into factors impacting ride-hailing emissions, including travel demand, land use, demographics, and transportation networks. It highlights the interplay among urban structure, multi-modal travel, and emissions, focusing on network features such as betweenness centrality and accessibility. Employing the COPERT (Computer Programme to Calculate Emissions from Road Transport) model, ride-hailing emissions are calculated from vehicle trajectory data. To mitigate statistical errors from multicollinearity, variable selection involves tests and correlation analysis. Geographically and temporally weighted regression (GTWR) with an adaptive kernel function is designed to understand key influencing mechanisms, overcoming traditional GTWR limitations. It can dynamically adjust bandwidth based on the spatio-temporal distribution of data points. Experiments in Shenzhen validate this approach, showing a 9.8% and 10.8% increase in explanatory power for weekday and weekend emissions, respectively, compared to conventional GTWR. The discussion of findings provides insights for urban planning and low-carbon transport strategies.

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Spatial-modal scenarios of greenhouse gas emissions from commuting in Hong Kong

Cited by 18 publications

References 21 publications

Analysis of Socio-spatial Differentiation in Transport Mode Choice Preferences

Analysis of Socio-spatial Differentiation in Transport Mode Choice Preferences

Transport geography, climate change and space: opportunity for new thinking

Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study

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