Spatial Heterogeneous Characteristics of Ridesharing in Beijing–Tianjin–Hebei Region of China

Ma, Ye; Yu, Biying; Xue, Meimei

doi:10.3390/en11113214

Cited by 10 publications

(5 citation statements)

References 52 publications

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“…For example, if people foregoes their car they might change their expenditure in fuel, car maintenance and insurance. Ma et al ( 2018 ) found that carpooling’s potential CO 2 emissions reduction in Beijing was 612.8 × 10 3 t due to lower vehicle ownership rates and modal shifts. Schelte et al ( 2021 ) proposed that the quantification of environmental impacts should consider household or individual mobility budgets before and after they start using shared mobility.…”

Section: Resultsmentioning

confidence: 99%

“…Resource depletion was measured from two perspectives: fuel and material. Impacts from fuel depletion occurred mostly in the use phase and showed a decrease, largely linked to greater efficiency of vehicles in the shared fleets, modal shifts, and a reduction in distances traveled ( Table 4 and Table 5 ) (Baptista et al, 2014 ; Chen & Kockelman, 2016 ; Lausselet & Brattebø, 2021 ; Ma et al, 2018 ; Te & Lianghua, 2020 ; Yu et al, 2017 ; Zhang et al, 2021 ). In Lisbon, energy use from car sharing could be reduced from 125 to 82 or 67 GJ per year if vehicles were hybrid or electric, respectively (Baptista et al, 2014 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Environmental impacts of shared mobility: a systematic literature review of life-cycle assessments focusing on car sharing, carpooling, bikesharing, scooters and moped sharing

Arbeláez Vélez

2023

Transport Reviews

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Evidence about the environmental impacts of shared mobility is fragmented and scattered. In this article a systematic literature review is presented. The review focuses on assessments that use Life-Cycle Assessment to quantify the environmental impacts of car sharing, carpooling, bikesharing, and scooter/moped sharing. The results of these assessments were analyzed, as well as the factors that influence these impacts. Business-to-consumer car sharing, peer-to-peer car sharing, carpooling, bikesharing, and scooter/moped sharing can all cause gains and losses in terms of changing the environmental impacts of passenger transportation. The findings presented here refute unconditional claims that shared mobility delivers environmental benefits. Factors that influence changes in environmental impacts from passenger transportation from shared mobility include travel behaviour, the design of shared mobility modes, and how such schemes are implemented, as well as the local context. Local governments and shared mobility organisations can benefit from the analysis presented here by deepening their understanding of these factors and considering the life-cycle phase where the greatest impacts are caused.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Environmental impacts of shared mobility: a systematic literature review of life-cycle assessments focusing on car sharing, carpooling, bikesharing, scooters and moped sharing

Arbeláez Vélez

2023

Transport Reviews

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“…The spatial matching of flow data was essential for mapping mobile carbon emission sources; this is a distinctive feature of this study compared with traditional spatial analyses. Previous studies have focused on the spatial analysis of traffic carbon emissions across large administrative units (e.g., cities and provinces) due to a lack of complete statistical data and uncertainties in spatial positioning (Xu and Xu, 2021;Ma and Kang, 2017). Conversely, this study used O-D traffic data to locate mobile carbon sources by road section and explore the emissions characteristics of highways across multiple dimensions, by vehicle types, in individual cities, and for small-scale road sections.…”

Section: Discussionmentioning

confidence: 99%

“…Further, administrative divisions limit the spatial scope. Most spatial analyses are carried out at large scales using provinces and cities as basic units (Ma and Kang, 2017); thus, refining estimates for specific road sections is impossible. 2) Vehicle classification is unreliable and unfavorable for identifying the variability in carbon emissions.…”

Section: Methodologies For Evaluating Carbon Emissions From Transport...mentioning

confidence: 99%

Mapping Highway Mobile Carbon Source Emissions Using Traffic Flow Big Data: A Case Study of Guangdong Province, China

Zhang

et al. 2022

Front. Energy Res.

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The continuously growing transportation sector has become the second largest, yet increasing, industrial emissions source of CO2, posing serious challenges to global environmental security. Among the various transport modes, road transportation yields the highest cumulative level of CO2 emissions. However, these emissions have not been sufficiently investigated in previous studies, especially with respect to analyses from the perspective of vehicle emission sources. This can make source management and emissions reduction difficult. To address these methodological issues, this study aims to build a highway traffic carbon emissions monitoring and spatial analysis system, employing the mobile carbon sources concept, and establish a carbon emissions model encompassing all types of passenger and freight vehicles based on interstation O-D traffic flow data recorded by the toll collection network, to calculate vehicle carbon emissions and create a mobile carbon source emissions map. Empirical analyses in Guangdong Province revealed that, compared with conventional studies, the mobile carbon source emission mapping approach can accurately identify vehicle types with higher emissions while assisting with source management. Of the average total daily carbon emissions from all types of vehicles that use highways (15,311 t), 57.10% originated from freight vehicles (8,743 t) while passenger vehicles contributed 42.90%. By specific vehicle type, emissions mainly originated from small and medium-sized vehicles, including Class I passenger vehicles (i.e., cars) and Class I and III freight vehicles. Further, the proposed method could locate road sections characterized by high carbon emissions. High-emission sections in Guangdong Province were mainly spatially autocorrelated, with peak aggregations on national highways; near economically developed and densely populated areas; and adjacent to surrounding airports, ports, and overpass roads. This study improves the scientific and spatial analytical accuracy for carbon emissions measurements of highway vehicles, thus informing source management and sustainable development, as well as providing technical support for attaining carbon neutrality in China.

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“…Ma et al [49] combine DiDi Chuxing trip data for the Beijing-Tianjin-Hebei area with survey data in which respondents indicate their alternative modes of transport to conduct a lifecycle analysis. They find that while ride-hailing does not lead to an increase in travel demand, in Beijing and Tianjin, riders substitute away from the subway and bus, respectively.…”

Section: Empirical Studies 32121 Public Transitmentioning

confidence: 99%

Impacts of ride-hailing on energy and the environment: a systematic review

Sheldon,

Dua

2024

Environ. Res. Lett.

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Ride-hailing has expanded substantially around the globe over the last decade and is likely to be an integral part of future transportation systems. We perform a systematic review of the literature on energy and environmental impacts of ride-hailing. In general, empirical papers find that ride-hailing has increased congestion, vehicle miles traveled, and emissions. However, theoretical papers overwhelmingly point to the potential for energy and emissions reductions in a future with increased electrification and pooling. Future research addressing the gap between observed and predicted impacts is warranted.

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Spatial Heterogeneous Characteristics of Ridesharing in Beijing–Tianjin–Hebei Region of China

Cited by 10 publications

References 52 publications

Environmental impacts of shared mobility: a systematic literature review of life-cycle assessments focusing on car sharing, carpooling, bikesharing, scooters and moped sharing

Environmental impacts of shared mobility: a systematic literature review of life-cycle assessments focusing on car sharing, carpooling, bikesharing, scooters and moped sharing

Mapping Highway Mobile Carbon Source Emissions Using Traffic Flow Big Data: A Case Study of Guangdong Province, China

Impacts of ride-hailing on energy and the environment: a systematic review

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