China has been suffering from serious air pollution for years in response to the rapid industrialization and urbanization. Notably Beijing is one of the most polluted capitals in the world. Hence, the focus of the study area is on Beijing. In the first stage, we analyze spatial and temporal characteristics of air pollution of the 6 cities while in the second stage the Granger causality test is applied to investigate whether air pollution of a city is affected by its neighbors, and vice versa. The findings are the following. Overall, AQI values are high in winter and early spring while low in summer and autumn. Among the 6 cities, Baoding is the major contributor to air pollution in this entire area. Besides, Granger causality test results show that there is a unidirectional relationship running from Baoding to Beijing and a bidirectional relationship between Beijing and Tianjin. In other words, apart from local air pollutants, for example, exhaust gas, air quality of Beijing is affected by air pollution of Tianjin, and vice versa. However, regarding the relationship between Beijing and Baoding, air quality of Beijing is just affected by air pollution of Baoding, since Baoding is much polluted than Beijing.

In the shadow of the energy crisis and environmental degradation, energy intensity is a hot topic in academic circles in China. The energy intensity distribution map of China indicates the fairly large geographic disparities globally and clustering locally in some areas, ascending from the southeast regions to the northwest provinces. Although energy intensity and its determinants vary from place to place, few studies have been made from the spatial perspective. Determinates of energy intensity and spatial spillover effects should be taken into consideration. Controlling for seven exogenous variables (per capita GDP; the share of the secondary sector; foreign direct investment; international trade, energy price, the share of coal, and transport sector) and their spatial lags, we apply a spatial Durbin model to test for spatial spillover effects among energy intensity and exogenous variables from a panel of 29 Chinese provinces over 1998 to 2014. We find that per capita GDP has an insignificant and negative direct and indirect effect, but has a significant and negative total effect on energy intensity. The share of the secondary sector and the share of coal are found to have significant and positive direct and indirect effects on energy intensity. Foreign Direct Investment (FDI) and Trade have significant and negative direct and indirect effects on energy intensity. The direct effect of energy price is found to be significantly positive while the indirect effect is negative. Only the direct effect of the Transport variable is significant and positive. The results of this study offer some theoretical evidence for differential localized policy making related to reduction in energy intensity.

To respond to the adverse impact of climate change on agricultural total factor productivity, the question of how to adopt actively appropriate strategies is particularly critical for the stakeholders. However, the previous researchers have paid more attention to investigating the measure methods, regional differences, and determinants of Chinese agricultural total factor productivity, but the possible impact of climate change factors like rainfall, temperature, and evaporation on regional agricultural total factor productivity in China have not yet received the attention that they deserve. Furthermore, more importantly, the study on how to take active measures to reduce and mitigate the negative effects from climate change is relatively small. Therefore, in allusion to the above-mentioned problems, using the data envelopment analysis and building a spatial panel data model embedded with climate change factors, this paper calculated Chinese agricultural total factor productivity and then explored the possible impact of climate change on regional agricultural total factor productivity at a provincial level in China. Results mainly show that the impact of some factors, like annual total precipitation, average temperature in the growing season, and evaporation intensity on regional agricultural total factor productivity, are all very significant and negative, which suggests that the more precipitation, the higher the temperature is, and the higher evaporation intensity would lower agricultural total factor productivity in China. Furthermore, in order to response to mitigate the adverse effects from climate change on agricultural total factor productivity, local governments should continue to increase financial support for the local agricultural economic development, because this action could be beneficial for the related stakeholders in improving agricultural total factor productivity. Summing up, our evidence study would provide an important basic theory basis in terms of increasing agricultural total factor productivity and promoting regional agricultural economic development in China.

China’s rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO₂) pollution, which has led to greater environmental pressures. In order to mitigate the environmental pressures caused by NO₂ pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO₂ pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO₂ pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO₂ pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO₂ pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO₂ pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO₂ pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.

The SARS-CoV-2 virus first emerged in late 2019 and has since spread quickly throughout China and become a global pandemic. As the situation with COVID-19 has evolved, wearing a face mask in public has grown commonplace. Using the technology acceptance model (TAM) as a foundation, this study introduces three new variables, namely, perceived risk, social pressure, and social image, to establish an extended model for investigating the factors that influence if residents wear masks. A total of 1200 questionnaires were distributed in China, from 1 February to 30 May 2020, through China’s largest online platform. The results indicate the following: 1. Residents’ positive attitude towards mask wearing promotes their behavioral intention to wear masks. 2. Perceived risk, social pressure, and social image have a positive impact on attitude towards mask wearing. 3. The intention to wear masks and attitude were both positively influenced by perceived usefulness. 4. The perceived usefulness is more influential in rural than urban groups, in terms of behavioral intention. This article proposes that public education on the facts related to the coronavirus, the threats posed by the COVID-19 pandemic to health, and the usefulness of face masks in preventing the transmission of COVID-19 could increase residents’ intention to wear a mask.

The past four decades have witnessed rapid growth in the rate of energy consumption in China. A great deal of energy consumption has led to two major issues. One is energy shortages and the other is environmental pollution caused by fossil fuel combustion. Since energy saving plays a substantial role in addressing both issues, it is of vital importance to study the intrinsic characteristics of energy consumption and its relationship with economic growth. The topic of the nexus between energy consumption and economic growth has been hotly debated for years. However, conflicting conclusions have been drawn. In this paper, we provide a novel insight into the characteristics of the growth rate of energy consumption in China from a multi-timescale perspective by means of adaptive time-frequency data analysis; namely, the ensemble empirical mode decomposition method, which is suitable for the analysis of non-linear time series. Decomposition led to four intrinsic mode function (IMF) components and a trend component with different periods. Then, we repeated the same procedure for the growth rate of China’s GDP and obtained four similar IMF components and a trend component. In the second stage, we performed the Granger causality test. The results demonstrated that, in the short run, there was a bidirectional causality relationship between economic growth and energy consumption, and in the long run a unidirectional relationship running from economic growth to energy consumption.