Exploring transport carbon futures using population microsimulation and travel diaries: Beijing to 2030

Evaluating transport policy for cities in developing countries is often constrained by data availability that limits the use of conventional appraisal models. Here, we present a new ‘bottom-up’ methodology to estimate transport CO₂ emission from daily urban passenger travel for Beijing, a megacity with relatively sparse data on travel behaviour. A spatial microsimulation, based on an activity diary survey and two sample population censuses, is used to simulate, for Beijing’s urban districts, a realistic synthetic population, and their daily travel and CO₂ emission over 2000–2010. This approach provides greater insight into the spatial variability of transport CO₂ emission than has previously been possible for Beijing, and further, enables an examination of the role of socio-demographics, urban form and transport developments in contributing to emissions over the modelled period.Using the 2000–2010 CO₂ emission estimates as a baseline, CO₂ emissions from passenger travel are then modelled to 2030 under scenarios exploring politically plausible strategies on transport (public transport infrastructure investment, and vehicle constraint), urban development (compaction) and vehicle technology (faster adoption of clean vehicle technology). The results showed that, compared to the trend scenario, employing both transport and urban development policies could reduce total passenger CO₂ emission to 2030 by 24%, and by 43% if all strategies were applied together. The study reveals the potential of microsimulation in emission estimation for large cities in developing countries where data availability may constrain more traditional approaches.     

Low-carbon futures for Shenzhen’s urban passenger transport: A human-based approach

Shenzhen, one of China’s leading cities, has the potential to be a model for achieving China’s ambitious CO₂ emission reduction targets. Using data from a travel diary survey in Shenzhen in 2014, we develop a human-based agent model to conduct a scenario study of future urban passenger transport energy consumption and CO₂ emissions from 2014 to 2050. Responses to different policy interventions at the individual level are taken into account. We find that with current policies, the carbon emissions of the urban passenger transport sector in Shenzhen will continuously increase without a peak before 2050. Strengthening 21 transport policies will help Shenzhen to peak the carbon emissions by 2030 for passenger transport. Among these policies, the car quota policy and the fuel economy standard are essential for achieving a carbon peak by 2030. In addition, a package of seven policies, including fewer car quotas, a stricter fuel economy standard, raising parking fees, limiting parking supply, increasing EV charging facilities and subway lines, and improving public transport services, is sufficient to peak carbon emissions by 2030, although at an emissions level higher than for the 21 policies.     

Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process

Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.     

Key factors influencing the global passenger transport dynamics using the AIM/transport model

A bottom-up passenger transport model named AIM (Asia-pacific Integrated Model)/Transport model is developed by incorporating behavioral parameters and transportation technological details. This model is based on discrete based choice modelling covering 17 global regions soft-linked with the AIM/CGE (Computable General Equilibrium) model. In this paper, the model is used to assess the impact of various factors like travel time, energy efficiency improvement, load factor, mode preference along with environmental awareness factors on transport demand, energy and emissions. The modelling assessment results show that travel speed and land-use patterns have significant impact on the travel demand. High occupancy rate and shift towards the mass-transit system result in energy and emissions reduction. Implementation of carbon tax aligned with the two-degree target results in a 22% cumulative emission reduction from 2005 to 2100 relative to the baseline case. However, the reduction potential can be increased to 42% by combining behavioral and technology related mitigation options like mass-transit system speed improvement, transit oriented development, efficiency improvement, preference towards eco-friendly technologies and high vehicle occupancy.     

基于LEAP模型的城市客运交通能耗和污染物排放预测

我国交通运输业能源消耗量与污染物排放量日益增多,交通部门的节能减排措施研究迫在眉睫。本论文基于LEAP模型构建了某城市客运交通能耗与污染物排放模型,并预测了基准情景、政策情景、技术情景及综合情景下该城市客运交通2014-2030年之间的能耗及污染物排放情况。研究表明,三种发展情景下该城市能源需求量及污染物排放较基准情景均有减少,其中综合情景效果最佳,综合情景在2030年的能源消耗与CO2排放可分别减少56.98%和54.55%,CO、HC、NOx与PM2.5可分别减排67.39%、67.27%、51.43%与75.38%。因此应大力发展公共交通,尤其是轨道交通,同时限制私家车的无节制发展,并推广节能环保技术以改善终端利用层次能源结构。     

Carbon emission from urban passenger transportation in Beijing

Urban passenger transport significantly contributes to global greenhouse gas emissions, especially in developing countries owing to the rapid motorization, thus making it an important target for carbon reduction. This article established a method to estimate and analyze carbon emission from urban passenger transport including cars, rail transit, taxis and buses. The scope of research was defined based on car registration area, transport types and modes, the stages of rail transit energy consumption. The data availability and gathering were fully illustrated. A city level emission model for the aforementioned four modes of passenger transport was formulated, and parameters including emission factor of electricity and fuel efficiency were tailored according to local situations such as energy structure and field survey. The results reveal that the emission from Beijing’s urban passenger transport in 2012 stood at 15 million tonnes of CO₂, of which 75.5% was from cars, whereas car trip sharing constitutes only 42.5% of the total residential trips. Bus travel, yielding 28.6 g CO₂, is the most efficient mode of transport under the current situations in terms of per passenger kilometer (PKM) emission, whereas car or taxi trips emit more than 5 times that of bus trips. Although a decrease trend appears, Beijing still has potential for further carbon reduction in passenger transport field in contrast to other cities in developed countries. Development of rail transit and further limitation on cars could assist in reducing 4.39 million tonnes CO₂ emission.     

Should subsidies to urban passenger transport be increased? A spatial CGE analysis for a German metropolitan area

In many countries passenger transport is significantly subsidized in a variety of ways for various reasons. The objective of this paper is to examine efficiency, distributional, environmental (CO₂ emissions) and spatial effects of increasing different kinds of passenger transport subsidies discriminating between household types, travel purposes and travel modes. The effects are calculated by applying a numerical spatial general equilibrium approach calibrated to an average German metropolitan area. In extension to most studies focusing on only one kind of subsidy, we compare the effects of different transport subsidies within the same unified framework that allows to account for two features not yet considered simultaneously in studies on transport subsidies: endogenous labor supply and location decisions. Furthermore, congestion, travel mode choice, travel related CO₂ emissions and institutional details regarding the tax system in Germany are taken into account. The results suggest that optimal subsidy levels are either small or even zero. While subsidizing public transport is welfare enhancing, subsidies to urban road traffic reduce aggregate urban welfare. Concerning the latter it is shown that making investments in urban road infrastructure capacity or reducing gasoline taxes may even be harmful to residents using predominantly automobile. In contrast, pure commuting subsidies hardly affect aggregate urban welfare, but distributional effects are substantial. All policies cause suburbanization of city residents and (except for subsidizing public transport) contribute to urban sprawl by raising the spatial imbalance of residences and jobs but the effect is relatively small. In addition, the policies induce a very differentiated pattern regarding distributional effects, benefits of landowners and environmental effects.     

Policy packages for modal shift and CO2 reduction in Lille,France

This paper proposes different policy scenarios to cut CO₂ emissions caused by the urban mobility of passengers. More precisely, we compare the effects of the ‘direct tool’ of carbon tax, to a combination of ‘indirect tools’ – not originally aimed at reducing CO₂ (i.e. congestion charging, parking charges and a reduction in public transport travel time) in terms of CO₂ impacts through a change in the modal split. In our model, modal choices depend on individual characteristics, trip features (including the effects of policy tools), and land use at origin and destination zones. Personal “CO₂ emissions budgets” resulting from the trips observed in the metropolitan area of Lille (France) in 2006 are calculated and compared to the situation related to the different policy scenarios. We find that an increase of 50% in parking charges combined with a cordon toll of €1.20 and a 10% travel time decrease in public transport services (made after recycling toll-revenues) is the winning scenario. The combined effects of all the policy scenarios are superior to their separate effects.     

The impact of land-use mix on residents' travel energy consumption: New evidence from Beijing

Policies that encourage mixed land use are widely believed to make transport more energy efficient. However, few studies have directly examined the impacts of land-use heterogeneity on travel energy consumption at the individual level. Moreover, the definition and measures of land-use heterogeneity are debated. This paper aims to fill these gaps using the large city of Beijing, China, as a case study. Three types of land use are examined in terms of their effects on individual residents’ travel energy consumption. The results suggest that high land-use diversity and a good jobs-housing balance significantly reduces commuting travel. Interestingly, highly heterogeneous retail and housing areas may have high travel energy use, as residents are more likely to go shopping. There are obvious spatial variations in these effects. Residents of suburban ‘newtowns’, where the jobs-housing balance is particularly good, consume less travel energy. The results suggest that decreased use of conventional planning patterns, such as the socialist danwei system, and increasing urban sprawl, bring new challenges to achieving transport efficiency. Mixed land-use policies can be an effective solution to these challenges.     

A land use and transportation integration method for land use allocation and transportation strategies in China

In this paper, we will first review literature of the land use and transportation interaction and then develop a new land use allocation methodology called Three Stages-Two-Feedback Method (Integration Method) for both land use allocation and the transportation policy options with a practical implementation. Then we apply this method in an urban general planning project in China with more than 1.2 million populations. In this project, we evaluated three land use allocation strategies and three transportation policy options using two application tools (with and without feedbacks) using this method implemented in a land use planning system UPlan and a transportation planning system Emme. The results show that the use of the feedback method (Application Two) results in a vehicle distance reduction and the increase in the service coverage area of transit bus stops at the same time. Due to the use of transportation accessibility and the congestion measures with a MSA implementation, the accessibility measure shows a convergent process over iterations. This nice feature can be used for alternative comparisons. Future research subjects are also discussed.     

Co-benefits of low carbon passenger transport actions in Indian cities: Case study of Ahmedabad

Rising population, income and urbanization are increasing urban passenger transport demand in India. Energy and emissions intensities associated with conventional transport are no longer sustainable vis-a-vis energy security, air quality and climate change. Cities are seeking transport roadmaps that jointly mitigate these risks. Roadmaps vary across cities, but approach to delineate actions is common: (i) ‘representative vision’ that articulates long-term goals, (ii) methods for comparative scenarios assessment, and (iii) quantification of co-benefits to prioritize actions. This paper illustrates application of quantitative modeling to assess development and environmental co-benefits for Ahmedabad city. The paper constructs two transport scenarios spanning till 2035. The bifurcating themes are: (i) Business-as-Usual (BAU) and Low Carbon Scenario (LCS). The quantitative assessment using Extended Snapshot (ExSS) Model shows that transport activity shall result in four-fold increase in energy demand under BAU from 2010 to 2035. Three key contributors to CO₂ mitigation under LCS in merit order are: (i) fuel switch, including decarbonized electricity, (ii) modal shift, and (iii) substitution of travel demand. Scenarios analysis shows that LCS improves energy security by reducing oil demand and also delivers air quality co-benefits – reducing 74% NOx and 83% PM_2.5 from the passenger transport sector compared to BAU in 2035. Finally, the paper argues that cities in developing countries can leverage carbon finance to develop sustainable and low carbon mobility plans that prevent adverse infrastructure and behavioral lock-ins and prompt low carbon development.     

Powertrain technologies and their impact on greenhouse gas emissions in key car markets

The role alternative car technologies may play in effectively tackling the problem of climate change is still highly uncertain. This paper aims at investigating possible impacts of car powertrain technologies on future energy demand and its corresponding greenhouse gas emissions until 2030. A system dynamics model covering nine car technologies in China, France, Germany, India, Japan and the United States was applied, with a focus on electric cars. Four main scenarios are constructed and sensitivity analysis undertaken. Greenhouse gas emissions from cars in the six countries are simulated to reach up to 2.6 gigatonnes in 2030 (a 13–32% increase between 2020 and 2030, depending on the scenario). The main conclusion from model-based policy analysis is that electric cars may have a positive contribution to emissions mitigation in the passenger road transport system. However, greenhouse gas emissions from cars arising from the combined effect of car manufacturing and scrappage and electricity generation processes are expected to grow more dramatically. As a result, actions that support both low-emission (re-)manufacturing and clean electricity generation are needed. These results complement accurate but static life cycle assessments and open the discussion for dynamic model assumptions.     

Energy use in passenger transport in OECD countries: Changes since 1970

This paper analyzes some of the changes that took place in the structure of energy use for passenger travel in industrialized countries. Data is presented on energy use and travel activity for the four major modes of travel — automobile, bus, rail and air — for eight OECD countries: the United States, Japan, the United Kingdom, West Germany, France, Italy, Sweden, and Norway. We use the Laspeyres and Divisia indices to analyze the causes of the change in energy use between 1970 and 1987. The total change in energy use for travel is explained by changes in domestic passenger transport volumes, the mix of modes of travel, and the energy intensities of each mode. We have found two important effects that have a fundamental impact on energy use for travel since 1970. First, shifts among modes of transport towards more energy-intensive ones and large increases in volumes of travel (measured in passenger-kilometers) increased energy use for travel in many OECD countries, often more rapidly than the overall growth in GDP. Second, energy intensities, measured in mJ/passenger-kilometer, of passenger transport fell only in a few countries between 1970 and 1987. Even though individual automobiles have become more energy-efficient, greater size, power, and weight, worsening traffic conditions in Japan and Europe, and fewer people in cars restrained or even offset efficiency improvements. Particularly notable are the increases in intensities in Japan and Germany. The most important exception to this trend was the United States, but the intensities of land-based travel remain higher there than in most other countries. These findings lead to a pessimistic outlook for future energy use for travel. After all, if little or no energy was saved during the decades of high fuel prices, what can be expected in the 1990s?     

Optimization of tunnel tolls in land use and transport planning

The task of transport planning is to determine cost-effective methods of providing and improving mobility, which can include minimizing traffic congestion. A cost-effective solution to transport problems should consist of a land use pattern, a transport system an a set of road pricing policies that together bring demand and supply into balance in an efficient and equitable way. The conventional approach aimed to produce comprehensive, long-term plans for land use and transport in considerable detail, but tended to ignore the role of road pricing policy, thus ending up with solutions that might not be efficient or economical. This feature of sub-optimal road pricing policy is accentuated by the overall growth in car use, which has generated problems with the efficient use of road space. This paper presents a computer analysis system (or model) which will enable the analysis of coordinated tunnel toll pricing policies by optimising an “objective function” while satisfying the associated and other constraints. The possibility of integrating the optimal road pricing policies in the land use and transport planning are discussed. A case study based on Hong Kong data demonstrates the efficiency of optimizing tolls on two of the three harbour crossing tunnels in Hong Kong.     

Structure and dynamics of non-suburban passenger travel demand in Indian railways

In this paper we derive long run structural relationships for all the three classes, viz. upper, second and ordinary second class, of non-suburban long distance passenger transport demand for Indian railways using annual time series data for 1970–1995. We employ some of the recent developments in multivariate dynamic econometric time series modeling including estimation of long-run structural cointegrating relationships, short-run dynamics and measurement of the effects of shocks and their persistence on evolution of the dynamic passenger transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modeling framework, which allows for endogeneity of regressors. The demand systems are found to be stable for all the classes in the long run and they converge to equilibrium in a period of around 2–4 years after a typical system-wide shock. Any disequilibrium in the short-run is corrected in the long-run with adjustments in passenger transport demand and the price variable, i.e. real rate charged per passenger kilometer. Results show that travel demand in all classes would rise with income, although the rise is less than proportionate in the case of ordinary class. High price elasticity in long-run and short-run impulse responses indicate that passenger fare hike could lead to substantial decline in travel demand leading to decline in revenue earnings of the railways. This revised version was published online in June 2006 with corrections to the Cover Date.     

Transport infrastructure costs in low-carbon pathways

The rate and manner in which transport infrastructure (e.g. roads, railway tracks, airports) is deployed, will play an important role in determining energy demand, greenhouse gas emissions and the economic impact of the transport sector. This paper describes an exercise where the costs of infrastructure deployment for the transport sector have been incorporated into the IMACLIM-R Global E3 IAM. In addition to adding these costs, the modelling of the criteria for the deployment of infrastructure for roads has also been improved. It is found that this model recalibration results in a more accurate baseline as compared to historically observed data (2001–2013) for investments in energy demand, road infrastructure, and passenger kilometers travelled. Regarding macroeconomic effects, it is found that the imposition of a carbon emission trajectory to 2100 cause GDP to decrease relative to the newly calibrated baseline – this is a standard IAM result. However, when the deployment of infrastructure for roads and air travel is further constrained, the GDP loss is less than with a fixed carbon emission trajectory only. This is because early restriction of infrastructure for roads and air travel allows an expansion of public transport infrastructure which is adequate to meet low-carbon transport service demand whereas when less public transport infrastructure is available, more costly mitigation investments must be made in other parts of the economy. This suggests that restricting infrastructure deployment as a complementary policy to carbon pricing, lowers the cost of mitigation.     

Spatiotemporal influence of land use and household properties on automobile travel demand

Understanding the patterns of automobile travel demand can help formulate policies to alleviate congestion and pollution. This study focuses on the influence of land use and household properties on automobile travel demand. Car license plate recognition (CLPR) data, point-of-interest (POI) data, and housing information data were utilized to obtain automobile travel demand along with the land use and household properties. A geographically and temporally weighted regression (GTWR) model was adopted to deal with both the spatial and temporal heterogeneity of travel demand. The spatial-temporal patterns of GTWR coefficients were analyzed. Also, comparative analyses were carried out between automobile and total person travel demand, and among travel demand of taxis, heavily-used private cars, and total automobiles. The results show that: (I) The GTWR model has significantly higher accuracy compared with the Ordinary Least Square (OLS) model and the Geographically Weighted Regression (GWR) model, which means the GTWR model can measure both the spatial and temporal heterogeneity with high precision; (II) The influence of built environment and household properties on automobile travel demand varies with space and time. In particular, the temporal distribution of regression coefficients shows significant peak phenomenon; and (III) Comparative analyses indicate that residents’ preference for automobiles over other travel modes varies with their travel purpose and destination. The above findings indicate that the proposed method can not only model spatial-temporal heterogeneous travel demand, but also provide a way to analyze the patterns of automobile travel demand.     

The significant impacts on traffic and emissions of ferrying children to school in Beijing

After having implemented numerous regulations, e.g., coercive policies on vehicle use and purchase, it is becoming increasingly difficult to find further potential to control vehicle emissions in Beijing, as the air quality is still poor. This research provides a different approach for policy-makers to reduce vehicle emissions by managing demand. We found that parents ferrying their children to and from school is an important but long-neglected contributor to traffic congestion and vehicle emissions. This phenomenon is very common in China because of the social culture. In this research, parallel tests during both the school season and the non-school season were adopted, and emissions in both seasons were calculated based on travel demand and emission models. The results revealed that emissions factors (in g/km) for criteria pollutants and CO₂ increased by over 10% during rush hours during the school season due to traffic condition deterioration compared with non-school season. Daily HC, CO, NO_x, PM and CO₂ emissions from the passenger car fleet were 8.3%, 7.8%, 6.4%, 6.3% and 6.5% higher compared with those during the non-school season, respectively. These differences are greater than the total vehicular emission reduction by other control measures in 2014 in Beijing. For policy makers, providing safe and efficient ways to ferry children would be a useful and harmonious strategy for future vehicle emission control.     

The impact of high-speed rail investment on economic and environmental change in China: A dynamic CGE analysis

This study investigates the impact of high-speed rail investment on the economy and environment in China using a computable general equilibrium (CGE) model. The analysis is implemented in a dynamic recursive framework capturing long-run capital accumulation and labor market equilibrium. A national level impact was simulated through direct impact drivers including land use conversion, output expansion, cost reduction, productivity increase, transport demand substitution and induced demand. The results suggest that rail investment in China over the past decade has been a positive stimulus to the economy, while the effect on CO₂ emissions generation has been large. Overall, the economic impacts of rail investment are achieved primarily through induced demand and output expansion, whereas the contribution from a reduction of rail transportation costs and rail productivity increases were modest. In addition, negligible negative impacts were found from land use for rail development and the substitution effect among other modes. Emissions reduction from substitution of rail for other modes was small and offset by output expansion due to lowered rail transport costs and induced demand.     

The relationship between land use and automobile travel utility: A Multiple Indicators Multiple Causes approach

The relationship between land use and the utility of automobile travel is examined by refining the utility concept, particularly by combining the microeconomic utility theory, which is concerned with the disutility of travel, and the perspective on the positive utility. A conceptual model is accordingly developed and then adjusted considering different purposes of travel. The purpose-specific models are tested through a Multiple Indicators Multiple Causes approach in Seoul, Korea, using datasets from a sample survey and geographic information systems. The major finding is that land use affects the utility mainly by changing synergy and affective utility rather than instrumental utility, which encompasses disutility variables. Among land use variables, the utility is found to be the most sensitive to the number of transit facilities for commuting and shopping travel and land use balance for leisure travel.