Transportation & Urban Planning

Rapid urban expansion in China has created both opportunities and challenges for promoting active transport in urban residential communities. Previous studies have shown that the urban form at the city scale has affected active transport in Chinese cities. However, there is less agreement about how the physical and social variations of neighborhood types should be addressed. This research investigates the four most representative neighborhood types found in Chinese cities: traditional mixed-use, slab block work-unit, gated community, and resettlement housing. Household travel diaries conducted in Chengdu in 2016 were analyzed using binary logistic regressions, supplemented by informal onsite interviews. The findings indicate significant variations in the use and accessibility of active transport in each neighborhood type for non-work trips. This suggests that each neighborhood type may need different strategies for promoting active transport: (1) the traditional mixed-use neighborhoods are in need of intensified urban retrofitting projects to reclaim public open space; (2) the work-unit could benefit from comprehensive plans rather than a patchwork of projects; (3) while opening up gated communities can improve porosity across neighborhoods and promote active transport, the more pressing issue may be their inability to keep up with the transportation needs of the residents; and (4) residents of resettlement housing should have better access to employment using transit and non-motorized modes.

In China a centralized planning culture has created similar neighborhoods across the country. Using a survey of 1,048 individuals conducted in 2016 in Chengdu—located in a carefully conceptualized typology of neighborhood forms—we analyzed the associations between individual and neighborhood characteristics and active or non-motorized transport behavior. Using several multiple logistic and multi-level models, we show how neighborhoods were categorized and the number of categories or neighborhood types affected the magnitude of the associations with active transport but not the direction. People taking non-work trips were more likely to use active compared with motorized modes in all neighborhood types. Neighborhood type was significant in models, but so were many other individual-level variables and infrastructural and locational features such as bike lanes and location near the river. Of the 3-D physical environment variables, floor area ratio (a proxy for density) was only significant in one model for non-work trips. Intersection density and dissimilarity (land use diversity) were only significant in a model for work trips. This study shows that to develop strong theories about the connections between active transport and environments, it is important to examine different physical and cultural contexts and perform sensitivity analyses. Research in different parts of China can help provide a more substantial base for evidence-informed policy-making. Planning and design recommendations related to active transport need to consider how neighborhoods, built environments, and personal characteristics interact in different kinds of urban environments.

Big data is increasingly regarded as a new approach for understanding urban informatics and complex systems. Today, there is unprecedented data availability, with detailed remote-sensed data on the built environment and rich mineable web-based sources in the form of social media, web mapping, information services and other sources of unstructured "big data". 

This book brings together a group of international contributors to consider the geographical implications of mobility, wellbeing and development within and across Chinese cities through location-based big data perspectives. The degree of urban sprawl, productive density and vibrancy can be reflected from location-based social media big data. The challenge is to identify, map and model these relationships to develop cities at different places in the urban hierarchical system that are more sustainable. This edited book aims to tackle these issues through two inter-related geographical scales: inter-city level and intra-city level.

The text is designed for graduate courses in planning, geography, public policy and administration, and for international researchers who are involved in urban and regional economics and economic geography.

In this paper, we look at differences in travel behavior and location characteristics across income in Chengdu, China at two points of time, 2005 and 2016, using household travel surveys. Specifically, we compare changes over time for different income groups for Chengdu in 2005 and 2016. We find that walking or biking remains the most common mode for all income groups but higher-income households appear to have more choices depending on the proximity of their neighborhood to downtown. We also find that both average local and average regional access have worsened since 2005. Furthermore, it appears that there is less economic diversity within neighborhoods in 2016 when compared to 2005, with more locations appearing to have 40% or more of low-, middle-, or high-income households than in the past. Finally, we find that low-income households and older trip makers are more likely to walk or bike and that high-income households are the most likely to own cars and use motorized modes. Built environment characteristics like mixed land use appear to significantly reduce travel time in 2016 but do not result in higher non-motorized transport mode share. We contribute to existing literature by evaluating changes in the relationship of built environment and travel behavior during a period of rapid urbanization and economic growth in a Chinese city.

China pledges to peak CO₂ emissions by 2030 or sooner under the Paris Agreement to limit global warming to 2 °C or less by the end of the century. By examining CO₂ emissions from 50 Chinese cities over the period 2000–2016, we found a close relationship between per capita emissions and per capita gross domestic product (GDP) for individual cities, following the environmental Kuznets curve, despite diverse trajectories for CO₂ emissions across the cities. Results show that carbon emissions peak for most cities at a per capita GDP (in 2011 purchasing power parity) of around US$21,000 (80% confidence interval: US$19,000 to 22,000). Applying a Monte Carlo approach to simulate the peak of per capita emissions using a Kuznets function based on China’s historical emissions, we project that emissions for China should peak at 13–16 GtCO₂ yr⁻¹ between 2021 and 2025, approximately 5–10 yr ahead of the current Paris target of 2030. We show that the challenges faced by individual types of Chinese cities in realizing low-carbon development differ significantly depending on economic structure, urban form and geographical location.

In this paper, we discuss walkable cities from the perspective of urban planning and design in the era of digitalization and urban big data. We start with a brief review on historical walkable cities schemes; followed by a deliberation on what a walkable city is and what the spatial elements of a walkable city are; and a discussion on the emerging themes and empirical methods to measure the spatial and urban design features of a walkable city. The first part of this paper looks at key urban design propositions and how they were proposed to promote walkability. The second part of this paper discusses the concept of walkability, which is fundamental to designing a walkable city. We emphasize both the physical (walkways, adjacent uses, space) and the perceived aspects (safety, comfort, enjoyment), and then we look at the variety of spatial elements constituting a walkable city. The third part of this paper looks at the emerging themes for designing walkable cities and neighborhoods. We discuss the application of urban big data enabled by growing computational powers and related empirical methods and interdisciplinary approaches including spatial planning, urban design, urban ecology, and public health. This paper aims to provide a holistic approach toward understanding of urban design and walkability, re-evaluate the spatial elements to build walkable cities, and discuss future policy interventions.

Developing less auto-dependent urban forms and promoting low-carbon transportation (LCT) are challenges facing our cities. Previous literature has supported the association between neighborhood form and low-carbon travel behaviour. Several studies have attempted to measure neighborhood forms focusing on physical built-environment factors such as population and employment density and socio-economic conditions such as income and race. We find that these characteristics may not be sufficiently fine-grained to differentiate between neighborhoods in Chinese cities. This research assesses characteristics of neighborhood spatial configuration that may influence the choice of LCT modes in the context of dense Chinese cities. Urban-form data from 40 neighborhoods in Chengdu, China, along with a travel behaviour survey of households conducted in 2016, were used to generate several measures of land use diversity and accessibility for each neighborhood. We use principle component analysis (PCA) to group these variables into dimensions that could be used to classify the neighborhoods. We then estimate regression models of low-carbon mode choices such as walking, bicycling, and transit to better understand the significance of these built-environment differences at the neighbourhood level. We find that, first, members of households do choose to walk or bike or take transit to work provided there is relatively high population density and sufficient access to public transit and jobs. Second, land-use diversity alone was not found to be significant in affecting LCT mode choice. Third, the proliferation of gated communities was found to reduce overall spatial connectivity within neighborhoods and had a negative effect on choice of LCT.

We estimate China urban household energy demand as part of a complete system of consumption demand so that it can be used in economy-wide models. This allows us to derive cross-price elasticities unlike studies which focus on one type of energy. We implement a two-stage approach and explicitly account for electricity, domestic fuels and transportation demand in the first stage and gasoline, coal, LPG and gas demand in the second stage. We find income inelastic demand for electricity and home energy, but the elasticity is higher than estimates in the rich countries. Demand for total transportation is income elastic. The price elasticity for electricity is estimated to be −0.5 and in the range of other estimates for China, and similar to long-run elasticities estimated for the U.S.

The relationship between dense urban development, often represented by high-rise buildings, and its location vis-à-vis metro stations reflects the connection between transportation infrastructure and land use intensity. Existing literature on high-rise buildings has focused either on developed countries or on cities where urban and public transit developments have occurred in an uncoordinated manner. This paper examines the following questions: What is the spatial proximity and spatial correlation between high-rise buildings and metro stations in different stages of development in various parts of the city? What were some of the factors that resulted in the observed patterns? The results suggest that buildings constructed after 2000 and buildings within the urban core/Shanghai Proper districts had a greater spatial proximity to the metro stations. However, the spatial correlation, measured by the number of high-rise buildings within a 500-meter buffer from the nearest metro stations and the time-distance to these stations, is stronger in the outer districts than in the urban core. These differences can be accounted for by Shanghai’s stages of urban development, the existence of metro infrastructure when high-rise development was undertaken, and the city’s land use policies. This case study sheds light on the relationship between high-density developments and metro systems in other large cities in China and other developing countries where rapid urban development coincides with the establishment of a comprehensive public transit system.

Reducing CH4 emissions is a major global challenge, owing to the world-wide rise in emissions and concentration of CH4 in the atmosphere, especially in the past decade. China has been the greatest contributor to global anthropogenic CH4 emissions for a long time, but current understanding towards its growing emissions is insufficient. This paper aims to link China's CH4 emissions during 2005–2012 to their socioeconomic determinants by combining input-output models with structural decomposition analysis from both the consumption and income perspectives. Results show that changes in household consumption and income were the leading drivers of the CH4 growth in China, while changes in efficiency remained the strongest factor offsetting CH4 emissions. After 2007, with the global financial crisis and economic stimulus plans, embodied emissions from exports plunged but those from capital formation increased rapidly. The enabled emissions in employee compensation increased steadily over time, whereas emissions induced from firms' net surplus decreased gradually, reflecting the reform on income distribution. In addition, at the sectoral level, consumption and capital formation respectively were the greatest drivers of embodied CH4 emission changes from agriculture and manufacturing, while employee compensation largely determined the enabled emission changes across all industrial sectors. The growth of CH4 emissions in China was profoundly affected by the macroeconomic situation and the changes of economic structure. Examining economic drivers of anthropogenic CH4emissions can help formulate comprehensive mitigation policies and actions associated with economic production, supply and consumption.

The effects of metro system development and urban form on housing prices highly depend on the spatial temporal conditions of urban neighborhoods. However, scholars have not yet comprehensively examined these interactions at a neighborhood-scale. This study assesses metro access, urban form, and property value at both the district- and neighborhood-level. The study area is Pudong, Shanghai, where metro system development has coincided with rapid urban growth. Two hundred and seventy-nine neighborhoods from 13 districts of Shanghai are randomly selected for the district-level investigation and 31 neighborhoods from Pudong are selected for the neighborhood-level investigation. The analysis of variance shows that metro access is more positively correlated to property price in Pudong than other districts. The Pearson correlation, principle component, and ordinary least square regression analyses show that while accessibility attributes have a positive influence on housing prices, neighborhood characteristics also exhibit a pronounced impact on property price change over time. This study extends our knowledge on how metro system development interacts with landuse efficiency and discusses planning policies that correspond to different stages of development.

Global anthropogenic CH₄ emissions have witnessed a rapid increase in the last decade. However, how this increase is connected with its socioeconomic drivers has not yet been explored. In this paper, we highlight the impacts of final demand and international trade on global anthropogenic CH₄ emissions based on the consumption‐based accounting principle. We find that household consumption was the largest final demand category, followed by fixed capital formation and government consumption. The position and function of nations and major economies to act on the structure and spatial patterns of global CH₄ emissions were systematically clarified. Substantial geographic shifts of CH₄emissions during 2000‐2012 revealed the prominent impact of international trade. In 2012, about half of global CH₄ emissions were embodied in international trade, of which 77.8% were from intermediate trade and 22.2% from final trade. Mainland China was the largest exporter of embodied CH₄ emissions, while the USA was the largest importer. Developed economies such as Western Europe, the USA and Japan were major net receivers of embodied emission transfer, mainly from developing countries. CH₄emission footprints of nations were closely related to their human development indexes (HDIs) and per capita gross domestic products (GDPs). Our findings could help to improve current understanding of global anthropogenic CH₄ emission increases, and to pinpoint regional and sectoral hotspots for possible emission mitigation in the entire supply chains from production to consumption.

 

Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM≤2.5 micrometers in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.

 

Current Chinese policy promotes the development of both electricity-propelled vehicles and carbon-free sources of power. Concern has been expressed that electric vehicles on average may emit more CO₂ and conventional pollutants in China. Here, we explore the environmental implications of investments in different types of electric vehicle (public buses, taxis and private light-duty vehicles) and different modes (fast or slow) for charging under a range of different wind penetration levels. To do this, we take Beijing in 2020 as a case study and employ hourly simulation of vehicle charging behaviour and power system operation. Assuming the slow-charging option, we find that investments in electric private light-duty vehicles can result in an effective reduction in the emission of CO₂ at several levels of wind penetration. The fast-charging option, however, is counter-productive. Electrifying buses and taxis offers the most effective option to reduce emissions of NO_x, a major precursor for air pollution.

Development of urban networks of cities and towns has received attention including discussions of tensions between population concentrations and overlaps with environmentally sensitive and disaster-prone areas. Moreover, certain development in broad regions of China, such as its deltas, has become a subject of debate. Contrary to some assumptions, this development within places like the Changjiang Delta (also known as the Yangtze River Delta) has proceeded in a relatively incremental manner. However, at this juncture, controlled development of larger cities, like Shanghai, has shifted to more conventional urbanization pathways forward involving larger city expansions. Nevertheless, further urban growth management appears to depend on development and maintenance of a well-balanced network of large, medium, and small-scaled cities and towns. An important aspect of this development involves definition of the Changjiang Delta region itself, and in particular, alongside its likely further economic performance. To these ends, a scenario-based Cellular Automata model of spatial distribution is deployed, reflecting separate thematic projections. A baseline for economic performance is developed, incorporating measures of fixed-asset investment in urban service, revenue from urban maintenance, and Gross Domestic Product. Revelation of a well-performing network involves spatial distribution of development at various scales, and in various concentrations within the region, moreover, location of this development, largely perpendicular to well-travelled corridors, appears as a preferable outcome, contrary to earlier depictions along the major transportation corridors.

Vehicles have recently overtaken coal to become the largest source of air pollution in urban China. Research on mobile sources of pollution has foundered due both to inaccessibility of Chinese data on health outcomes and strong identifying assumptions. To address these, we collect daily ambulance call data from the Beijing Emergency Medical Center and combine them with an idiosyncratic feature of a driving restriction policy in Beijing that references the last digit of vehicles’ license plate numbers. Because the number 4 is considered unlucky by many in China, it tends to be avoided on license plates. As a result, days on which the policy restricts license plates ending in 4 unintentionally allow more vehicles in Beijing. Leveraging this variation, we find that traffic congestion is indeed 22% higher on days banning 4 and that 24-hour average concentration of NO₂ is 12% higher. Correspondingly, these short term increases in pollution increase ambulance calls by 12% and 3% for fever and heart related symptoms, while no effects are found for injuries. These findings suggest that traffic congestion has substantial health externalities in China but that they are also responsive to policy.