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This study compares the atmosphere-only HighResMIP simulations from FGOALS-f3-H (FGOALS) and MRI-AGCM3-2-S (MRI) with respect to tropical cyclone (TC) characteristics over the Western North Pacific (WNP) for the July–October months of 1985–2014. The focus is on investigating the role of the tropical easterly jet over the Western Pacific (WP_TEJ) in modulating the simulation biases in terms of their climatological distribution and interannual variability of WNP TC genesis frequency (TCGF) based on the analysis of the genesis potential index (GPI). Results show that the two models reasonably capture the main TC genesis location, the maximum center of frequency, and track density; however, their biases mainly lie in simulating the intense TCs and TCGF distributions. The MRI better simulates the wind-pressure relationship (WPR) but overestimates the proportion of super typhoons (SSTYs). At the same time, FGOALS underestimates the WPR and the proportion of SSTYs but better simulates the total WNP TC precipitation. In particular, FGOALS overestimates the TCGF in the northeastern WNP, which is strongly tied to an overestimated WP_TEJ and the enhanced vertical circulation to the north of its entrance region. In contrast, the MRI simulates a weaker WP_TEJ and vertical circulation, leading to a negative TCGF bias in most of the WNP. Both models exhibit comparable capability in simulating the interannual variability of WP_TEJ intensity, but the composite difference of large-scale atmospheric factors between strong and weak WP_TEJ years is overestimated, resulting in larger interannual anomalies of WNP TCGF, especially for FGOALS. Therefore, accurate simulations of the WP_TEJ and the associated oceanic and atmospheric factors are crucial to further improving WNP TC simulations for both models.

This research combines “big data” and “thick data” approaches to examine the correlation and causation between residential neighborhood features and people’s daily commuting and traveling patterns by integrating two datasets: household survey data and mobile phone data. We focus on “lilong” neighborhoods—a primary form of traditional residential neighborhood in central Shanghai. The characteristics of lilong neighborhoods are assessed using “thick data” from surveys in 105 lilongs, while residents’ daily activities are mapped out using “big data” from two weeks of mobile phone usage. We match these two datasets at neighborhood level based on their geospatial references. Four multinomial logistic regression models are developed to examine neighborhood effects on lilong residents’ daily activities. Our research confirms the major mechanisms of neighborhood effects and unravels their relative importance in shaping the patterns of residents’ daily activities. Conceptually, this study sheds new light on the understanding of how people’s life quality and wellbeing are affected by neighborhood characteristics through highlighting the importance of social interactions and the access to/quality of public facilities. Methodologically, incorporating household survey data (thick data) and mobile phone data (big data) is proven to be a novel and effective approach for examining neighborhood effects at a relatively large scale.

ABSTRACTOpen biomass burning (OBB) has a significant impact on the heavy haze pollution in Northeast China (NEC) in recent years, which requires the investigation of the spatiotemporal variations of OBB with different vegetation types to better monitor and control OBB in NEC. The MODIS C6 fire and land cover products, together with the emissions inventory from the Global Fire Assimilation System, were used in this study. The changes in the total number of MODIS fire points in NEC from 2003 to 2017 demonstrated a fluctuating but generally rising trend, with a peak during 2013–2017. Most fire points concentrated in two key periods, i.e. March–April (37%) and October–November (46%). The total number of crop residue burnings in March–April was basically slightly fluctuating and increased sharply from 2013, whilst the number in October–November had a fluctuating and upward trend until 2015, when a decline appeared. The amount of OBB in March–April was higher than that in October–November during 2016–17. OBB in Heilongjiang Province comprised a major proportion of all fires, which accounted for 70.7% from 2003 to 2017; however, the proportion was only 66.2% during 2013–2017. The largest proportion of all fires was in cropland (90.8%), then forest (5.3%) and grassland (3.1%). The cumulative emissions of fine particulate matter, nitrogen oxides, and ammonia from agricultural open burning in NEC reached 78.43 Gg, 24.9 Gg, and 13.7 Gg for March–April during 2013–17, respectively, which were close to those in October–November.

To encourage low-carbon travel, reduce car trips, and improve the urban traffic system, an understanding of travel mode choice for escorting children to and from school is important. Previous studies have focused mostly on walking and bicycling. However, the electric bicycle (e-bike—a mainstream escorting mode in China) rarely has been investigated. The factors that affect travel mode choice (walking, e-bike, or car) for escorting children to and from school are explored at the household level in a case study from Kunming, China. Intrahousehold interactions and parental space–time constraints are represented by the comparative working hours of the parents and space–time coordination in joint parent–child trips, respectively. A multinomial logit model was applied in mode analysis. The results reveal interesting phenomena in travel mode choices for escorting children to school. When the mother’s working hours are longer than the father’s, the travel mode choice for escorting a child to and from school is less likely to be walking or e-bike; in other words, a car is the preferred choice in mother-dominated households. The distance deviation between parents and children is a significant factor in choosing between a car or an e-bike. Children are more likely to be escorted by e-bikes as a result of distance deviations, and walking or e-bike is less likely to be chosen when the school is far from home. Results could be valuable for traffic demand management to reduce car use for trips to and from school. In addition, e-bike development should be considered seriously for the future development of China.

To encourage low-carbon travel, reduce car trips, and improve the urban traffic system, an understanding of travel mode choice for escorting children to and from school is important. Previous studies have focused mostly on walking and bicycling. However, the electric bicycle (e-bike—a mainstream escorting mode in China) rarely has been investigated. The factors that affect travel mode choice (walking, e-bike, or car) for escorting children to and from school are explored at the household level in a case study from Kunming, China. Intrahousehold interactions and parental space–time constraints are represented by the comparative working hours of the parents and space–time coordination in joint parent–child trips, respectively. A multinomial logit model was applied in mode analysis. The results reveal interesting phenomena in travel mode choices for escorting children to school. When the mother’s working hours are longer than the father’s, the travel mode choice for escorting a child to and from school is less likely to be walking or e-bike; in other words, a car is the preferred choice in mother-dominated households. The distance deviation between parents and children is a significant factor in choosing between a car or an e-bike. Children are more likely to be escorted by e-bikes as a result of distance deviations, and walking or e-bike is less likely to be chosen when the school is far from home. Results could be valuable for traffic demand management to reduce car use for trips to and from school. In addition, e-bike development should be considered seriously for the future development of China.

This article describes how national imaginaries of market reforms designed to accommodate fast-growing Chinese regions, collide with embedded formal and informal governance practices in less developed provinces. Whilst much of the literature examines the implications of new infrastructure on regional economics, the management of existing networks dominates decision-making in these more stagnant economies. Using interviews with key stakeholders involved in highway management in Heilongjiang across the provincial and local levels, this paper explores institutional change and persistence using path dependence and junctures as an organising lens. We find that the manner and timing of institutional changes are shaped by the need for political elites in the territorial governing body to reinforce their authority over the vertical functional systems within the jurisdiction. Institutional persistence beneath the changes demonstrates path dependence, related to (a) the vested interests due to power trading and interpersonal relationships and (b) the defence of personnel-related benefits derived from long-standing public institutions. Political and administrative factors rather than economic and technical factors are paramount in influencing transport management and institutional processes in China's less developed provinces.

Individuals have experienced various degrees of accessibility loss during the COVID-19 pandemic, which may consequently have influenced their mental health. Although efforts have been made to understand the mental health consequences of the pandemic and corresponding containment measures, the impacts of accessibility loss remain underexplored.

Knowing how workers return to work is a key policymaking issue for economic recovery in the post-COVID-19 era. This paper uses country-wide time-series mobile phone big data (comparing monthly and annual figures), obtained between February 2019 and October 2019 and between February 2020 and October 2020, to discover the spatial patterns of rural migrant workers’ (RMWs’) return to work in China’s three urban agglomerations (UAs): the Beijing–Tianjin–Hebei Region, the Yangtze River Delta and the Pearl River Delta. Spatial patterns of RMWs’ return to work and how these patterns vary with location, city level and human attribute were investigated using the fine-scale social sensing related to post-pandemic human mobility. The results confirmed the multidimensional spatiotemporal differentiations, interaction effects between variable pairs and effects of the actual situation on the changing patterns of RMWs’ return to work. The spatial patterns of RMWs’ return to work in China’s major three UAs can be regarded as a comprehensive and complex interaction result accompanying the nationwide population redistribution, which was affected by various hidden factors. Our findings provide crucial implications and suggestions for data-informed policy decisions for a harmonious society in the post-COVID-19 era.