Governance for Post-COVID-19 Carbon Reduction: A Case Study of the Transport Sector (preprint)

This article argues that carbon reduction goals in the transport sector should be achieved through the management of policy making and implementation processes, based on a transport-sector carbon emissions identity and the DIRECT approach. Based on the above identity, it is argued that carbon emissions from the transport sector can be reduced from the following six connected domains: (1) carbon intensity of energy consumption from transport, (2) energy consumption from transport, (3) transport pressures from life and business activities, (4) high-carbon life and business activities, (5) changing the needs in life and business, and (6) population policy. The DIRECT approach includes six steps: (1) Detect, (2) Inform/Intervene, (3) React, (4) Enlighten/Enforce/Evaluate, (5) Collaborate, and (6) Transfer, which should be applied to manage each of the above six domains. The “6-domain & 6-step” is proposed as an integrated policy framework for reducing carbon emissions from the transport sector in a seamless way.

The Effects of Whether Wearing the Mask in the Spreading Process of COVID-19

During the pandemic of COVID-19, wearing a mask, and keeping social distance become significant topics among the public, which are somewhat controversial. In this project, it discusses the fluid field changing when coughing, which will produce a large quantity of saliva. The project conducts series of experiments to prove the importance of wearing a mask when being in a crowd. The result of the project shows that wearing a mask can reduce the probability that saliva particles enter the breathing zone by 44.7%, which can largely reduce the affection rate of the virus.

The Spread of COVID-19 in China at Its Initial Stage: A Township-Level Analysis in Association with the Built Environment (preprint)

Background: The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) as well as other infectious diseases by facilitating human mobility and social contacts between infected and susceptible individuals. It can also provide a space that enables the direct transmission of pathogens to susceptible individuals. On the other hand, mobility data capturing interpersonal transmission at finegrain in large scale are not available. With the above background, we aim to assess the associations of key built environment factors, which create spaces for activities — “inferred activity” rather than “actually observed activity” — with the spread of COVID-19 in China at its initial stage. Method: In this nationwide study, we used a random forest (RF) approach to objectively assess the associations between the built environment factors and the spread of COVID-19. The spread is measured for 2 994 township-level administrative units in China by two indicators, the first of which is the ratio of cumulative infection cases (RCIC) that were confirmed until February 13, 2020 (14 days after stopping public transportation operations in all Chinese cities). The other indicator is the coefficient of variation of infection cases (CVIC) between February 6 and February 13, 2020, which reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 independent variables covering built environment attributes (e.g., urban facilities, transportation infrastructure, and land use), socioeconomic characteristics, and inter-city (from Hubei Province) population flow (PF). We also investigated the spatial agglomerations using a bivariate local indicators of spatial association (BiLISA) between the above two indicators of the spread. The data is from Amap, Resource and Environment Data Cloud Platform, Google Earth Engineer (GEE), Baidu direction lite API, Baidu Migration Production, Tencent, People's Daily, and other references.Findings: The spread of COVID-19 had obvious spatial agglomerations all across China. Clusters with both low RCIC and low CVIC at the same locations (Low-Low type) show the largest percentage, and are followed by the Low-High type. The Low-High type is more dangerous because clusters of low RCIC are surrounded by clusters with high CVIC which means that policy measures taken in the initial stage may be less effective to prevent/mitigate a rapid disease spread at these locations. The density of convenience shops, supermarkets, and shopping malls (DoCSS) along with PF were the two most important factors to RCIC, whereas PF was the most important factor to measure the policy effects (i.e., the CVIC). When the DoCSS as well as the density of road intersections (DoI), the density of gyms and sport centers (DoGSC) reach 21/km2, 72/km2, and 2/km2, respectively, positive associations of these density indicators with the RCIC become maximal and kept constant as the indicators further increase. Only after PF exceeds 75% its relationship with the RCIC starts showing a positive relationship with RCIC and the relationship keeps stable when the PF gets to 80%. For policy effects, the density of colleges and universities (DoCU) and the density of comprehensive hospitals (DoCH) had negative impacts on policy effects in the initial stage of the COVID-19 spread. Notably, when the DoCH was at less than 0.1/km2, it had a small positive trend. Accordingly, increasing betweenness centrality (BC) associated with increasing policy effect in the initial stage of COVID-19 spread before 5 000, and a reversed trend was observed after 5 000.Interpretation: First, the findings from this study do not support nationwide uniform measures against COVID-19 (e.g., lockdown and other restriction measures, or reopening of economic activities). Second, the built environment factors may be associated with mitigating or accelerating the spread of COVID-19. Third, the revealed thresholds of different built environment factors in association with the spread of COVID19 present insightful evidence on how to integrate urban and public health planning for future pandemics. Funding: This study is financially supported by two funds from Japan Science and Technology Agency. One of these funds is the J-RAPID Collaborative Research/Survey Program for Urgent Research framework which is titled, “Impacts of COVID-19 on the transport and logistics sector and countermeasures.” The other fund is the Ethical, Legal, and Social Implications/Issues (ELSI) research framework, “Responsible Innovation with Conscience and Agility,” which is titled, “Overcoming Vulnerability and Restoring Social Justice in Community and Re-designing Cities by Introducing Social Distancing."Declaration of Interests: The authors declare no competing interests.

Association of Transport Accessibility with the Spread of COVID-19 in Japan: An Analysis of the Period of January to September 2020 (preprint)

This study investigates how transport accessibility was associated with the accumulative number of confirmed cases of COVID-19 and its daily new cases, at the prefecture level, in Japan. The analysis was conducted month by month from January to September, 2020. A gravity model was estimated to measure transport accessibility and a nonlinear regression model was estimated how much transport accessibility is associated with the spread of COVID-19. Analysis results suggest that the Japanese government may need to pay sufficient attention to lifting various restrictions of both activity participation and trip making, both within cities and across cities, for preventing further spread of this virus in Japan.

Transport Policymaking against COVID-19 and Future Public Health Pandemics: A PASS Approach (preprint)

The novel coronavirus outbreak of 2019/2020 (COVID-19) has had wide-reaching and unprecedented impacts on the transport sector over the whole world. At present, there is no globally-agreed timeframe for when this pandemic will end. It is necessary to figure out how to address the current and near-future potential impacts in a relatively comprehensive and seamless way. Here, the author proposes a PASS (P: Prepare–Protect–Provide; A: Avoid–Adjust; S: Shift–Share; S: Substitute–Stop) approach for policymaking against COVID-19 and future public health threats. First, the PASS approach is illustrated conceptually, and then policy measures are recommended by referring to the past and ongoing best practices. Finally, policymaking challenges and research issues are discussed.

Testing Case Number of Coronavirus Disease 2019 in China with Newcomb-Benford Law (preprint)

The coronavirus disease 2019 bursted out about two months ago in Wuhan has caused the death of more than a thousand people. China is fighting hard against the epidemics with the helps from all over the world. On the other hand, there appear to be doubts on the reported case number. In this article, we propose a test of the reported case number of coronavirus disease 2019 in China with Newcomb-Benford law. We find a $p$-value of $92.8\%$ in favour that the cumulative case numbers abide by the Newcomb-Benford law. Even though the reported case number can be lower than the real number of affected people due to various reasons, this test does not seem to indicate the detection of frauds.