Evaluating air pollution exposure among cyclists: Real-time levels of PM2.5 and NO2 and POI impact.

Although cycling has numerous health benefits, the increased breathing volume and lack of protection from exposure to the environment while cycling poses health risks that cannot be disregarded. Previous studies evaluating the exposure of cyclists to air pollution have typically focused on assessing exposure to a single pollutant or exposure concentrations on specific urban routes, and have not performed a comprehensive assessment considering the distribution of cyclists. The present study used bicycle-sharing big data to conduct a more comprehensive and refined real-time population weighted exposure risk assessment of pileless bike sharing riders in Beijing. We quantified the spatial distribution of high exposure areas at different times and found that the exposure risk during the evening peak period was significantly higher than that during the morning peak and early morning periods, particularly in the city center and its environs. By establishing stepwise regression models, we identified the significant impact of various urban points of interest (POIs) on exposure risk, with sports venues, public toilets, educational institutions, scenic spots, and financial entities particularly influential at different time periods. Medical institutions and shopping venues have a significant negative impact on the exposure levels of PM2.5 and NO2 among cyclists in most cases. These findings emphasize the need for targeted pollution control strategies. The aim of this study is to mitigate the impact of air pollution on cyclists and create a healthier cycling environment. The research results can provide new ideas for urban health planning and support scientific decision-making for sustainable urban development.

How does the robot adoption promote carbon reduction?: spatial correlation and heterogeneity analysis.

Along with the continuous improvement of industrial intelligence, robots are widely used in various aspects of production and life, playing an essential role in achieving carbon reduction targets. However, the existing research on the carbon reduction effect of robots and its mechanism is limited. Therefore, this study aims to explore the impact of robot adoption on carbon emissions and analyzes the mechanism by taking 30 provinces in China from 2006 to 2019 as research objects. It found that robot adoption can significantly reduce carbon emissions. However, the degree of marketization plays a masking effect, which limits robots' carbon reduction effect to some extent. Furthermore, the carbon reduction effect of robot adoption is stronger in provinces with lower carbon emissions. Finally, robot adoption has a significant spatial spillover effect on neighboring regions. The improvement of robot adoption will positively affect the region's and surrounding areas' carbon emission reduction. The relevant findings provide empirical support for further deepening the policy implementation of robot-assisted carbon emission reduction.

Revisiting the gravity laws of inter-city mobility in megacity regions.

Inter-city mobility is one of the most important issues in the UN Sustainable Development Goals, as it is essential to access the regional labour market, goods and services, and to constrain the spread of infectious diseases. Although the gravity model has been proved to be an effective model to describe mobility among settlements, knowledge is still insufficient in regions where dozens of megacities interact closely and over 100 million people reside. In addition, the existing knowledge is limited to overall population mobility, while the difference in inter-city travel with different purposes is unexplored on such a large geographic scale. We revisited the gravity laws of inter-city mobility using the 2.12 billion trip chains recorded by 40.48 million mobile phone users' trajectories in the Jing-Jin-Ji Region, which contains China's capital Beijing. Firstly, unlike previous studies, we found that non-commuting rather than commuting is the dominant type of inter-city mobility (89.3%). Non-commuting travellers have a travel distance 42.3% longer than commuting travellers. Secondly, we developed more accurate gravity models for the spatial distribution of inter-city commuting and non-commuting travel. We also found that inter-city mobility has a hierarchical structure, as the distribution of inter-city travel volume follows Zipf's law. In particular, the hierarchy of non-commuting travel volume among the cities is more in line with an ideal Zipf distribution than commuting travel. Our findings contribute to new knowledge on basic inter-city mobility laws, and they have significant applications for regional policies on human mobility.

The Driving Mechanism of Urban Land Green Use Efficiency in China Based on the EBM Model with Undesirable Outputs and the Spatial Dubin Model.

Green development is necessary for building a high-quality modern economic system. The contribution of the article mainly includes the following three parts: First is the study on the urban land green use efficiency (ULGUE) in 30 provinces of China from 2008 to 2018 by adopting the epsilon-based measure (EBM) model with undesirable outputs to yield a more accurate and reasonable assessment result. In addition, the spatial agglomeration characteristics were analysed according to the spatial autocorrelation analysis. Thirdly, the spatial Durbin model was applied to analyse the driving factors of the WRGUE, which considers the spatial effects. The findings are as follows: (1) The regional differences in ULGUE were very significant, with the number decreasing from the coastal region to inland. (2) ULGUE showed a significantly positive spatial autocorrelation, and the spatial homogeneity was more significant than the spatial heterogeneity for ULGUE. (3) Economic development level, technical progress level, and urban population density have a significant impact on ULGUE, while the higher the proportion of the secondary industry in GDP, the lower the level of ULGUE. The research results may be a useful reference point for policymakers.

Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model.

Due to the pressure of global ecological degradation, the coordination of economic increase and ecological protection has drawn attention from policymakers and practitioners. Green economic efficiency (GEE) is a comprehensive index to measure economic, social, and environmental development. As China is the second-biggest economy in the world with high-energy consumption, it is necessary to investigate its green economy efficiency. In this paper, we innovatively adopt a super-SBM (slacks-based measure) model with undesirable outputs to calculate the GEE in 30 provinces of China between 2008 and 2017, and then comprehensively apply a spatial Dubin model (SDM) to investigated its influencing factors. The results showed that the overall GEE in China during the study period was at a low level with significant regional differences. The inter-regional GEE generally showed a gradient decreasing pattern of "East-Middle-West", which demonstrates a gradual decline from the East to the West in China. The trend of the national GEE initially dropped and then gradually stabilized over the study period. Foreign trade dependence and direct investment had significant positive effects on the GEE, while the secondary industry and urbanization level had a significant negative effect.