Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Spatial-temporal heterogeneity and driving factors of PM2.5 in China: A natural and socioeconomic perspective

Background

Fine particulate matter (PM_2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM_2.5 pollution is controversial in China.

Methods

In this study, we explored spatial-temporal characteristics and driving factors of PM_2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR).

Results

PM_2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m^-3 a^-1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM_2.5 concentrations. The driving force of socioeconomic factors on PM_2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM_2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM_2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013.

Conclusion

We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM_2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM_2.5 emission and put forward effective policy to alleviate haze pollution.

Green effects of research and development on industrial waste reduction during the production phase: Evidence from China and policy implications

Maintaining public health requires a clean environment; however, some industrial wastes can damage the water, atmosphere, and living environment seriously. To promote green development, policy makers in China have developed and implemented strict environmental regulations to limit the pollutant emissions and improve the environmental quality. Industrial producers implement research and development (R&D) activities to gain more profits in competitive markets. A comprehensive understanding of the green effects of R&D on different industrial wastes could provide important policy recommendations, especially regarding the coordination of innovative and green developments. In this study, the author empirically analyzed the influence of R&D input, including the intramural expenditure on R&D and full-time equivalent of R&D personnel, on industrial wastes, including the discharge of chemical oxygen demand (COD) and ammonia nitrogen, emission of sulfur dioxide, nitrogen oxides, and particulate matter, and generation of common industrial solid and hazardous wastes, based on the data from Chinese industrial sectors for 2016-2020. The main findings of empirical analyses were robust and indicated that R&D activities significantly reduced the emissions of all three industrial waste gases and decreased the discharge of COD; however, in the case of China, the partial effects on the discharge of ammonia nitrogen and the industrial solid wastes were not statistically significant. The green effects of R&D on different industrial wastes may vary and generally depend on environmental regulations, with various limitations. The most viable policy recommendations indicate that by expanding and initiating the green effect of R&D on different industrial wastes, innovative and green developments are more likely to be achieved in a coordinated manner. Additionally, this can also support special R&D activities, with the added benefit of actively developing cleaner technology to treat pollutant emissions. Development, while maintaining a clean environment to ensure public health, could be more sustainable if innovative activities reduce the production of industrial wastes. This study analyzes the green effects of R&D on industrial waste and can serve as a viable framework for future studies on sustainable development.

Haze pollution reduction in Chinese cities: Has digital financial development played a role?

Based on the exogenous shock of digital financial development in China in 2013, a difference-in-differences (DID) model is set up in this paper to investigate the causal relationship between digital financial development and haze pollution reduction. The finding of the paper is that a one standard deviation increase in digital finance after 2013 decreases the PM2.5 concentrations by 0.2708 standard deviations. After a number of robustness checks, like placebo tests, instrumental variable (IV) estimations, eliminating disruptive policies, and using alternative specifications, this causal effect is not challenged. In addition, this paper explores three potential mechanisms of digital finance to reduce haze pollution: technological innovation, industrial upgrading, and green development. Moreover, the heterogeneous effects signify that the usage depth of digital finance works best in haze pollution reduction. Digital finance has more positive effects in cities in the north and those with superior Internet infrastructure and higher levels of traditional financial development. However, the quantile regression estimates suggest that for cities with light or very serious haze pollution, the positive impact of digital finance is limited. These findings supplement the research field on the environmental benefits of digital finance, which provides insights for better public policies about digital financial development to achieve haze pollution reduction.