DAILY TRAVEL CARBON EMISSIONS AND MITIGATION POTENTIAL ANALYSIS USING INDIVIDUAL DATA IN BEIJING

摘要

OverviewAccounting for nearly a quarter of global energy-related greenhouse gas emissions, transport sector is acknowledged as a challenging sector for mitigation. Although currently China’s transport demand per capita is relatively low, its transport demand and related energy consumption and carbon emissions will grow much faster than developed countries. Fast growing income and consumption in China are the major drivers of increased travel distances and vehicle usage, which in turn is associated with rapidly growing transport emissions. Travel distances and the share of motorized travel at the aggregate level are determined by individual’s travel demand and mode choice collectively. However, existing studies about the emission accounting and mitigation potential in the transport sector do not pay enough importance at the individual level. Therefore, this study uses detailed individual trip data to account carbon emissions and mitigation potential from daily travel in Beijing, China.Our research contributes to existing studies in three aspects. First, very few studies have accounted individual carbon emissions from daily travel in China. Second, although travel distances directly determine the transport emission accounting, previous studies mainly relied on self-reported distances, which has been shown to be subjected to self-reporting bias. To eliminate self-reporting bias, we use Baidu Map to improve the measuring accuracy of travel distances. Third, our mitigation potential analysis innovatively uses trip-based information to look for mitigation opportunities. In this way, we avoid making simplified assumptions regarding individual’s behavioral change, and are able to obtain a more relevant and realistic mitigation potential assessment through mode shift under the current transport system.MethodsWe use questionnaire survey to collect citizen travel information in order to compute individual emissions from daily travel and analyze the difference and structure of emissions across individuals. The core part of the questionnaire is a trip record table for citizens to fill out yesterday’s trips. For each trip, the trip record table requires information including the trip purpose, departure time, transport modes used, and the origin, destination, and riding time for each transport mode. A sample of 1502 Beijing citizens are collected through both intercept survey and web survey. Carbon emissions of individual travel are calculated based on the distance traveled by each mode and the corresponding emission factors. Specifically, we use Baidu Map to improve the measuring accuracy of travel distances in order to eliminate self-reporting bias.For mitigation potential from behavioral change, we use trip-based information to determine the carbon mitigation potential of daily travel through mode shift under the current transport system. For each trip using high emission modes (i.e. car and taxi), we innovatively apply the online Baidu Map to find whether there exists low emission modes (i.e. walk, bike, or public transport) having comparable travel time with the original high emission modes to conduct the same trip. If such low emission modes exist, it is considered a mitigation opportunity. By summarizing the mitigation opportunities for all trips, we can get the mitigation potential for the whole sample.ResultsThe average individual carbon emissions from daily travel are computed as 1.46 kg/day•person and 2.40 kg/day•person for weekday and weekend. The individual travel emissions are distributed highly unevenly, with the 20% highest emitters produce 70% of emissions. We further analyzed the travel characteristics of high emitters and low emitters, and found that except for citizens who need to conduct more long distance trips (mainly trips longer than 20 km), the main contributor to the emissions of the rest high emitters are not the need of longer daily travel distances or the need to conduct more long distance trips, but the need to use cars more intensively for trips with similar distances compared with low emitters. Moreover, an average citizen emits higher CO_2 on weekend (2.40 kg/day•person) than weekday (1.46 kg/day•person). The is caused by more long distance trips and more frequent use of car on weekend. It is worth noting that on weekday 62% of travel distances are conducted by low emission modes and 33% of travel distances are conducted by cars. However, on weekend only 38% of travel distances are conducted by low emission modes and 59% of travel distances are conducted by cars.After figuring out the direct contributor to higher travel emissions, we investigate what individual, household, and environment characteristics are associated with high emitters.Regresion analysis reveals that being male, having higher income, and owning cars are associated with higher carbon emissions from daily travel on weekday and weekend. Citizen in their 30s and 40s generate more emissions on weekday. Living within fifth ring and having good accessibility to public transport are associated with lower emissions.The mitigation potential analysis found that if only considering travel time, about 20% to 25% of daily travel emissions can be mitigated by substituting high emission modes with low emission modes which have comparable travel time. However, the mitigation potential will be substantially constrained by practical barriers, e.g. the need to conduct sequential car trips. The implication is that mitigation policy for daily transport should not only focus on improving travel time of low emission modes to comparable levels with cars but also tackle practical barriers for car drivers to use low emission modes.ConclusionsOur study focuses on the carbon emission status and mitigation potential in the domain of daily travel in Beijing. Conducted at the micro individual level, this research is able to examine the difference and distribution of individual travel emissions, identify the direct contributor to high emissions, and analyze the characteristics associated with high emitters. Using a sample with the focus on working people, the average individual carbon emissions from daily travel are computed as 1.46 kg/day•person and 2.40 kg/day•person for weekday and weekend. The distribution of individual emissions is highly uneven, with 70% of emissions are produced by the 20% highest emitters. Except for citizens who need to conduct more long distance trips, the main contributor to the emissions of rest high emitters are not the need of longer daily travel distances or the need to conduct more long distance trips, but the need to use cars more intensively for trips with similar distances compared with low emitters. In order to target those high emitters, we used regression analysis and found that high emitters are associated with the characteristics of being male, having higher income, owning cars, and aging between 30s and 40s. On the other hand, living within fifth ring and having good accessibility to public transport are associated with lower emissions.These findings can help inform the design of effective and targeted mitigation policies in the domain of daily travel. Citizens with higher income and car ownership generate higher emissions, therefore they should be paid more attention in mitigation policy making. For those high emitters who need to conduct more long distance trips and thus use cars more intensively, it is important to understand why their travel distances are high and how to improve urban planning to reduce citizen travel distances. For those high emitters who frequently use cars to conduct short and medium distance trips, it is crucial to figure out what factors preventing these car drivers to use low emission modes. Our mitigation potential analysis reveals that for many trips low emission modes already have comparable travel time with cars and taxi, resulting in 20% to 25% mitigation potential if only travel time is considered. However, the mitigation potential will be substantially constrained by practical barriers, e.g. the need to conduct sequential car trips, the need to have a bike at hand in order to cycle, etc. The implication is that mitigation policy for daily transport should not only focus on improving travel time of low emission modes to comparable levels with cars but also tackle practical barriers for car drivers to use low emission modes.