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PM2.5 pollution has harmed the health and social lives of residents, and although evidence of PM2.5 pollution caused by human activities has been reported in a large body of literature, traditional econometric and spatial models can explain the contribution of a given factor from only a global perspective. Given this limitation, this study quantitatively investigated the effects of the spatiotemporal heterogeneity of various socioeconomic factors on PM2.5 pollution in 273 Chinese cities from 2010 to 2016 by exploratory spatial data analysis (ESDA) and geographically weighted regression (GWR). The spatiotemporal distribution pattern and intrinsic driving mechanism of city-level PM2.5 pollution were systematically examined. The results indicate the following: (1) The cities with high PM2.5 pollution are located north of the Yangtze River and east of the Hu line. A notable positive spatial correlation was observed between these cities, and nearly one-third of the cities are in the HH clustering area. (2) From the global regression point of view, population size and economic development are the main factors causing the deterioration and spread of PM2.5 pollution in Chinese cities, and population size undoubtedly exerts the strongest influence. Industrial structure, economic development, openness degree, urbanization and road intensity also play weak roles in promoting urban PM2.5 pollution. (3) The socioeconomic factors influencing pollution exhibit significant spatial heterogeneity. Specifically, the cities in which pollution is promoted by economic development are mainly concentrated in the northeast and western regions. The cities in which population size exerts a positive driving effect are in most regions, except for a few central and western cities. Three targeted strategies for developing more sustainable cities are comprehensively discussed by building on the understanding of the socioeconomic driving mechanism for PM2.5 pollution.
Dan Yan; Ying Kong; Peng Jiang; Ruixian Huang; Bin Ye. How do socioeconomic factors influence urban PM2.5 pollution in China? Empirical analysis from the perspective of spatiotemporal disequilibrium. Science of The Total Environment 2020, 761, 143266 .
AMA StyleDan Yan, Ying Kong, Peng Jiang, Ruixian Huang, Bin Ye. How do socioeconomic factors influence urban PM2.5 pollution in China? Empirical analysis from the perspective of spatiotemporal disequilibrium. Science of The Total Environment. 2020; 761 ():143266.
Chicago/Turabian StyleDan Yan; Ying Kong; Peng Jiang; Ruixian Huang; Bin Ye. 2020. "How do socioeconomic factors influence urban PM2.5 pollution in China? Empirical analysis from the perspective of spatiotemporal disequilibrium." Science of The Total Environment 761, no. : 143266.
The contradiction between the development of urban agglomerations and ecological protection has long been a challenging issue. China has experienced an astonishing expansion of its urban scale in the past 40 years, and nearly 783 million of the nation’s people now live in cities. Beijing–Tianjin–Hebei, the Yangtze River Delta and the Pearl River Delta have been prioritized to become world-class clusters by 2020. The health effects of air pollution in these three urban agglomerations are becoming increasingly formidable. Given these conditions, using the daily mean PM2.5 concentration in 40 cities from January 2014 to December 2016, this research explored the spatial–temporal characteristics of PM2.5 concentrations in these three urban agglomerations. The annual mean PM2.5 concentrations in Beijing–Tianjin–Hebei, the Yangtze River Delta and the Pearl River Delta are 35.39 µg/m3, 53.72 µg/m3 and 78.54 µg/m3, respectively. Compared with the other two urban agglomerations, abundant rainfall causes the Pearl River Delta to have the lowest PM2.5 level. Furthermore, a general regression neural network (GRNN) method is developed to predict the PM2.5 concentration in these clusters on the second day, with inputs including the average, maximum and minimum temperature; average, maximum and minimum atmosphere; total rainfall; average humidity; average and maximum wind speed; and the PM2.5 concentration measured 1 day ahead. The results indicate that the GRNN method can precisely predict the concentration level in these clusters, and it is especially useful for the Pearl River Delta, as the underlying influence mechanism is more specified in this cluster than in the others. Importantly, this 1-day-ahead forecasting of PM2.5 concentrations can raise awareness among the public to improve their precautionary behaviours and help urban planners to provide corresponding support.
Dan Yan; Ying Kong; Bin Ye; Haitao Xiang. Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China. Environmental Geochemistry and Health 2020, 43, 301 -316.
AMA StyleDan Yan, Ying Kong, Bin Ye, Haitao Xiang. Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China. Environmental Geochemistry and Health. 2020; 43 (1):301-316.
Chicago/Turabian StyleDan Yan; Ying Kong; Bin Ye; Haitao Xiang. 2020. "Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China." Environmental Geochemistry and Health 43, no. 1: 301-316.
With the more efficient involvement of both technology and policy factors in China’s whole industry-chain, the year 2020 is a key period for photovoltaic (PV) industry to achieve grid parity. In this context, COVID-19 may trigger a certain time-delay in new installed PV projects, thereby bringing an uncertain influence on the whole PV industry. To forecast the influence degree and influence cycle of COVID-19 on PV industry, this paper firstly clarifies the key features of epidemic situation as well as the basic rule of such pandemics’ transmission along industry-chain. Then this paper constructs a system dynamics model targeting at cost accounting of PV power generation under the influence of COVID-19 and thus forecasts the variation rules, superposition effects and influence cycle of levelized cost of energy (LCOE) of PV power generation and the operations cost of each sub-system. Empirical results show that PV industry has a lag response to the COVID-19 for 1 quarter and periodic response for 4 quarters, which is mainly embodied in the rise of short-term production cost. At the same time, the influence of COVID-19 on the upstream firms of PV industry is stronger than that on downstream firms. With the gradual recovery of whole industry-chain, LCOE of PV power generation will rapidly return to the previous expected level of grid parity by the end of 2020.
Yazhi Song; Tiansen Liu; Yin Li; Bin Ye. The influence of COVID-19 on grid parity of China’s photovoltaic industry. Environmental Geochemistry and Health 2020, 1 -16.
AMA StyleYazhi Song, Tiansen Liu, Yin Li, Bin Ye. The influence of COVID-19 on grid parity of China’s photovoltaic industry. Environmental Geochemistry and Health. 2020; ():1-16.
Chicago/Turabian StyleYazhi Song; Tiansen Liu; Yin Li; Bin Ye. 2020. "The influence of COVID-19 on grid parity of China’s photovoltaic industry." Environmental Geochemistry and Health , no. : 1-16.
Correspondence to Bin Ye. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions Ye, B., Zhang, X., Zhang, X. et al. Climate change, environmental impact, and human health. Environ Geochem Health (2020). https://doi.org/10.1007/s10653-020-00554-x Download citation Published: 07 April 2020 DOI: https://doi.org/10.1007/s10653-020-00554-x
Bin Ye; Xiaolei Zhang; Xiaoling Zhang; Chunmiao Zheng. Climate change, environmental impact, and human health. Environmental Geochemistry and Health 2020, 42, 715 -717.
AMA StyleBin Ye, Xiaolei Zhang, Xiaoling Zhang, Chunmiao Zheng. Climate change, environmental impact, and human health. Environmental Geochemistry and Health. 2020; 42 (3):715-717.
Chicago/Turabian StyleBin Ye; Xiaolei Zhang; Xiaoling Zhang; Chunmiao Zheng. 2020. "Climate change, environmental impact, and human health." Environmental Geochemistry and Health 42, no. 3: 715-717.
This study researches the dynamical location optimization problem of a mobile charging station (MCS) powered by a LiFePO 4 battery to meet charging demand of electric vehicles (EVs). In city suburbs, a large public charging tower is deployed to provide recharging services for MCS. The EV’s driver can reserve a real-time off-street charging service on the MCS through a vehicular communication network. This study formulates a multi-period nonlinear flow-refueling location model (MNFRLM) to optimize the location of the MCS based on a network designed by Nguyen and Dupuis (1984). The study transforms the MNFRLM model into a linear integer programming model using a linearization algorithm, and obtains global solution via the NEOS cloud CPLEX solver. Numerical experiments are presented to demonstrate the model and its solution algorithm.
Faping Wang; Rui Chen; Lixin Miao; Peng Yang; Bin Ye. Location Optimization of Electric Vehicle Mobile Charging Stations Considering Multi-Period Stochastic User Equilibrium. Sustainability 2019, 11, 5841 .
AMA StyleFaping Wang, Rui Chen, Lixin Miao, Peng Yang, Bin Ye. Location Optimization of Electric Vehicle Mobile Charging Stations Considering Multi-Period Stochastic User Equilibrium. Sustainability. 2019; 11 (20):5841.
Chicago/Turabian StyleFaping Wang; Rui Chen; Lixin Miao; Peng Yang; Bin Ye. 2019. "Location Optimization of Electric Vehicle Mobile Charging Stations Considering Multi-Period Stochastic User Equilibrium." Sustainability 11, no. 20: 5841.
Whether China can prevent its CO2 emissions from increasing by 2030 is critical for achieving the Paris Agreement's goal of limiting global warming below 2 °C. Understanding the growth and potential peak of CO2 emissions in various sectors and various provinces of China has great significance to formulate more targeted strategies on capping emissions on a national level. This issue has recently attracted increasing attention but remains far from being resolved. Therefore, this article critically reviews the current literature regarding sectoral- and provincial-level CO2 emission projections for China, to determine up-to-date study progresses and guide future studies. It has been concluded that China's various sectors and provinces present large gaps with respect to the time and the quantity to peak their CO2 emissions. Energy-extensive heavy industry sectors, such as cement, iron and steel, and electricity sectors, take the lead in capping CO2 emissions compared with service, transport, and building sectors. In addition, the eastern provinces are expected to achieve the peak of CO2 emissions prior to the central and western provinces, while more economically and technically advanced provinces reach this peak ahead of less developed and energy-producing provinces. Based on the significantly different dynamics and drivers of CO2 emissions, sectoral- and provincial-specific strategies on emission abatement are outlined for China. Moreover, four critical topics are highlighted for future study, including improvement of study methodology, detailed examination of CO2 emission trends in several key sectors and provinces, and in-depth exploration of the far-reaching impacts of capping CO2 emissions in China and associated countermeasures.
Jingjing Jiang; Bin Ye; Junguo Liu. Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research. Renewable and Sustainable Energy Reviews 2019, 112, 813 -833.
AMA StyleJingjing Jiang, Bin Ye, Junguo Liu. Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research. Renewable and Sustainable Energy Reviews. 2019; 112 ():813-833.
Chicago/Turabian StyleJingjing Jiang; Bin Ye; Junguo Liu. 2019. "Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research." Renewable and Sustainable Energy Reviews 112, no. : 813-833.