The impacts of household features on commuting carbon emissions: a case study of Xi’an, China

Lyu, Pu; Lin, Yongjie; Wang, Yuanqing

doi:10.1007/s11116-017-9829-4

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…The head of household labeled with younger age, employed, higher education and higher income is associated with higher household carbon emissions due to the expansion of social networks (Meng et al ., 2023). Scholars find there is a significant positive relationship between average age, education level of household members and household commuting carbon emissions by using Xi’an household survey data (Lyu et al ., 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing the determinants of household carbon footprints between rural and urban: evidence from China household survey data

Lv,

Liu,

Chai

2024

CAER

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PurposeUrbanization is driving the growth of China’s carbon footprint. It’s important to investigate what factors, how and to what extent, affect carbon footprints embedded in various categories of rural and urban households’ consumption.Design/methodology/approachWe employ an environmental extended input-output model to assess and compare the rural-urban household carbon footprints and perform a multivariant regression analysis to identify the varying relationships of the determinants on rural and urban household carbon footprints based on the panel data of Chinese households from 2012 to 2018.FindingsThe results show evidence of urbanity density effect on direct carbon footprints and countervailing effect on indirect carbon footprints. The old dependency ratio has no significant effect on rural family emissions but has a significantly negative effect on urban direct and indirect carbon footprints. A higher child dependency ratio is associated with less rural household carbon emissions while the opposite is true for urban households. Taking advantage of recycled fuel saves direct carbon emissions and this green lifestyle benefits urban households more by saving more carbon emissions. There is a positive relationship between consumption structure ratio and direct carbon footprints while a negative relationship with indirect carbon footprints and this impact is less significant for urban households. The higher the price level of water, electricity and fuel, the lower the rural household’s direct carbon footprints. Private car ownership consistently augments household carbon footprints across rural and urban areas.Originality/valueThis paper provides comprehensive findings to understand the relationships between an array of determinants and China’s rural-urban carbon emissions, empowering China’s contribution to the global effort on climate mitigation.

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Section: Literature Reviewmentioning

confidence: 99%

Comparing the determinants of household carbon footprints between rural and urban: evidence from China household survey data

Lv,

Liu,

Chai

2024

CAER

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“…There are two main types of intersection vehicle emission models: fuel-based [20,21] and speed-bases [13,22]. Fuel-based emission models are mainly applied to the measurement and analysis of transportation carbon emissions in cities, regions, and whole countries [23].…”

Section: Related Workmentioning

confidence: 99%

“…Based on the previously presented multi-objective optimization function for signal timing, along with its three constraints, the multi-objective optimization model at a single point intersection can be established as shown in Equation (20). (20) After the multi-objective optimization model at a single point intersection is built, the model needs to be solved to obtain the optimal solution.…”

Section: ) Signal Cycle Durationmentioning

confidence: 99%

A Multi-Objective Optimization Method for Single Intersection Signals Considering Low Emissions

Wang,

Zhao,

Zhang

et al. 2024

Sustainability

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The exponential growth of urban centers has exacerbated the prevalence of traffic-related issues. This surge has amplified the conflict between the escalating need for travel among individuals and the constricted availability of road infrastructure. Consequently, the escalation of traffic accidents and the exacerbation of environmental pollution have emerged as increasingly pressing concerns. Urban road intersections, serving as pivotal junctures for vehicle convergence and dispersal, have remained a focal point for scholarly inquiry regarding enhanced operational efficacy and safety. Concurrently, vehicles navigating intersections are subject to external influences, such as pedestrian crossings and signal controls, causing frequent fluctuations in their operational dynamics. These fluctuations contribute to heightened exhaust emissions, exacerbating air pollution and posing health risks to pedestrians frequenting these intersections. A reasonable signal timing scheme can enable more vehicles to pass through the intersection safely and smoothly and reduce the pollutants generated by transportation. Therefore, optimizing signal timing schemes at intersections to alleviate traffic problems is a topic that needs to be studied urgently. In this paper, the emission model based on specific power is analyzed. Through an analysis of the correlation between specific power distribution intervals and the emission rates of individual pollutants, it has been observed that vehicle emission rates are at their lowest during idle speed, progressively increasing with rising vehicle speeds. Investigation into specific power distribution based on variables, such as vehicle type, frequency of stops, and varying delays, has led to the deduction that the peak specific power of vehicles at intersections consistently occurs within the (0, 1) interval. Furthermore, it has been established that high-saturation intersections exhibit higher peak specific power compared to low-saturation intersections.

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Studies of the effectiveness of transport sector interventions in low‐ and middle‐income countries: An evidence and gap map

Malhotra

White

Cruz³

et al. 2021

Campbell Systematic Reviews

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Background There are great disparities in the quantity and quality of infrastructure. European countries such as Denmark, Germany, Switzerland, and the UK have close to 200 km of road per 100 km2, and the Netherlands over 300 km per 100 km2. By contrast, Kenya and Indonesia have <30, Laos and Morocco <20, Tanzania and Bolivia <10, and Mauritania only 1 km per 100 km2. As these figures show, there is a significant backlog of transport infrastructure investment in both rural and urban areas, especially in sub‐Saharan Africa. This situation is often exacerbated by weak governance and an inadequate regulatory framework with poor enforcement which lead to high costs and defective construction. The wellbeing of many poor people is constrained by lack of transport, which is called “transport poverty”. Lucas et al. suggest that up to 90% of the world's population are transport poor when defined as meeting at least one of the following criteria: (1) lack of available suitable transport, (2) lack of transport to necessary destinations, (3) cost of necessary transport puts household below the income poverty line, (4) excessive travel time, or (5) unsafe or unhealthy travel conditions. Objectives The aim of this evidence and gap map (EGM) is to identify, map, and describe existing evidence from studies reporting the quantitative effects of transport sector interventions related to all means of transport (roads, rail, trams and monorail, ports, shipping, and inland waterways, and air transport). Methods The intervention framework of this EGM reframes Berg et al's three categories (infrastructure, prices, and regulations) broadly as infrastructure, incentives, and institutions as subcategories for each intervention category which are each mode of transport (road, rail trams and monorail, ports, shipping, and inlands waterways, and air transport). This EGM identifies the area where intervention studies have been conducted as well as the current gaps in the evidence base. This EGM includes ongoing and completed impact evaluations and systematic reviews (SRs) of the effectiveness of transport sector interventions. This is a map of effectiveness studies (impact evaluations). The impact evaluations include experimental designs, nonexperimental designs, and regression designs. We have not included the before versus after studies and qualitative studies in this map. The search strategies included both academic and grey literature search on organisational websites, bibliographic searches and hand search of journals. An EGM is a table or matrix which provides a visual presentation of the evidence in a particular sector or a subsector. The map is presented as a matrix in which rows are intervention categories (e.g., roads) and subcategories (e.g., infrastructure) and the column outcome domains (e.g., environment) and subcategories as (e.g., air quality). Each cell contains studies of the corresponding intervention for the relevant outcome, with links to the available studies. Included studies were coded according to the interve...

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The impacts of household features on commuting carbon emissions: a case study of Xi’an, China

Cited by 14 publications

References 31 publications

Comparing the determinants of household carbon footprints between rural and urban: evidence from China household survey data

Comparing the determinants of household carbon footprints between rural and urban: evidence from China household survey data

A Multi-Objective Optimization Method for Single Intersection Signals Considering Low Emissions

Studies of the effectiveness of transport sector interventions in low‐ and middle‐income countries: An evidence and gap map

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