Transportation & Urban Environment

Yingying Lyu and Ann Forsyth. 2021. “Planning, Aging, and Loneliness: Reviewing Evidence About Built Environment Effects.” Journal of Planning Literature, August 2021. Publisher's VersionAbstract
Large numbers of people in many countries report being lonely with rates highest among the very old. Does the built environment affect loneliness among older people and if so, how? Using a scoping review, we examined associations between loneliness and built environments at the block, neighborhood, and city scales. The (1) neighborhood environment has received most attention. Research has also examined (2) urban contexts, (3) housing, and (4) transportation access. Findings are mixed with the stronger evidence that local resources, walkability, overall environment quality, housing options, and nearby transportation alternatives can help combat loneliness.
Haiyang Lin, Caiyun Bian, Yu Wang, Hailong Li, Qie Sun, and Fredrik Wallen. 2022. “Optimal planning of intra-city public charging stations.” Energy, 238, Part C, Pp. 121948. Publisher's VersionAbstract
Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.
Faan Chen, Jiaorong Wu, Xiaohong Chen, and Chris Nielsen. 2021. “Disentangling the impacts of the built environment and self-selection on travel behavior: An empirical study in the context of different housing types.” Cities, 116, September, Pp. 103285. Publisher's VersionAbstract
Due to spatial heterogeneity worldwide, results from studies examining the effect of residential self-selection on travel behavior vary substantially. As a result of housing reform, the unique housing allocation system in China is a prime example of a context where the self-selection effect may conflict with international knowledge. Using a sample of 3836 residents, whom are living in Transit-Oriented Development (TOD) and non-TOD neighborhoods in Shanghai, this study untangles the effects that the built environment and residential self-selection have on travel behavior, in the context of diversified housing types in urban China. Specifically, this paper employs propensity score matching (PSM) to quantitate the relative importance of the built environment itself, verses residential self-selection, in influencing travel behavior for each of the housing types. The results show that the residential self-selection effect in the four types of housing (work-unit, commodity, public, and replacement) accounts for 15.2%, 30.7%, 18.5%, and 5.9% of the total impact on vehicle kilometers traveled (VKT), respectively. These findings expand the international database of point estimates in the relative contribution of self-selection toward the impact on travel behavior across global contexts, providing a comprehensive framework for similar studies on self-selection in other parts of the world.
Rong Ma, Bin Chen, Chenghe Guan, Jing Meng, and Bo Zhang. 2018. “Socioeconomic determinants of China’s growing CH4 emissions.” Journal of Environmental Management, 228, 15 December 2018, Pp. 103-116. Publisher's VersionAbstract
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.
Chenghe Guan and Peter Rowe. 2017. “In pursuit of a well-balanced network of cities and towns: A case study of the Changjiang Delta Region.” Environment and Planning B: Urban Analytics and City Science, 48, 3, Pp. 1-19. Publisher's VersionAbstract
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.
Chenghe Guan and Peter Rowe. 2020. “Multi-criteria locational analysis for retail development in small towns.” In The Geography of Mobility, Wellbeing and Development: Understanding China’s Transformations through Big Data, 1st ed., Pp. 220. London: Routledge. Publisher's VersionAbstract

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.

Chenghe Guan, Richard Peiser, Shikyo Fu, and Chaobin Zhou. 2021. “New towns in China: The Liangzhu story.” In New Towns for the Twenty-First Century: A Guide to Planned Communities Worldwide, Richard Peiser and Ann Forsyth, eds. University of Pennsylvania Press. Publisher's Version