The Spatial and Heterogeneity Impacts of Population Urbanization on Fine Particulate (PM2.5) in the Yangtze River Economic Belt, China

Effects of urbanisation on PM2.5 concentrations: A systematic review and meta-analysis

While urbanisation greatly improves a population's quality of life, it also has significant effects on urban air pollution. Previous studies have determined how urbanisation affects PM2.5 concentrations; the findings, however, have not been consistent. This study conducts a meta-analysis to systematically organise existing research and draw more conclusive and broadly applicable results regarding the impact of different factors of urbanisation on PM2.5 concentrations. The main research findings are as follows: (1) the Environmental Kuznets Curve (EKC) is proven to hold true in terms of the effect of population and land urbanisation on PM2.5 concentrations, while there is no consistent conclusion on the non-linear relationship between economic urbanisation and PM2.5 concentrations; (2) publication bias is evident in research on the economic and comprehensive urbanisation dimensions under linear assumptions; (3) there are notable heterogeneities in existing research in this field. The meta-regression model further indicates that model design, sample design, and publication characteristics might be responsible for these heterogeneities. This study innovatively applies a meta-analysis to investigate the effect of urbanisation on PM2.5 concentrations. The findings will contribute to scholars designing more rigorous research frameworks in this field.

Multidimensional effects of urbanization on PM2.5 concentration in China

Recently, the contradiction between urbanization and the air environment has gradually attracted attention. However, most existing studies have explored the impact of single urbanization factors, such as population, the economy, or land, on PM_2.5 and ignored the impact of multidimensional urbanization on PM_2.5 concentration. Moreover, the heterogeneity in the mechanisms responsible for the PM_2.5 concentration caused by multidimensional urbanization has not been thoroughly studied in different regions in China. Therefore, we investigate the spatial-temporal evolution characteristics of PM_2.5 concentration in China during 1998-2019 by spatial analysis and dynamic panel models based on the environmental Kuznets curve (EKC). Then, we study the effects of multidimensional urbanization on PM_2.5 concentration, and analyze the dominant factors in China's eight economic regions. During the study period, the PM_2.5 concentration in China fluctuated before 2013 and gradually decreased thereafter. The PM_2.5 concentration has significant regional differences in China. Spatially, the PM_2.5 concentration is higher in the north than in the south and higher in the east than in the west. Additionally, there is a significant spatial spillover effect. Both population urbanization and economic urbanization show an inverted U-shaped relationship with PM_2.5 concentration in China, which is consistent with the classical EKC theory. Due to other socioeconomic factors, the PM_2.5 concentration tends to decrease linearly with increasing land urbanization rate. The effects of urbanization on the PM_2.5 concentration in the eight economic regions in China show significant differences. The effect of land urbanization on the PM_2.5 concentration is dominant in the Middle Yangtze River region, that of economic urbanization is dominant in northwestern China, and that of population urbanization is dominant in the remaining regions in China.

"Green" economic development in China: quantile regression evidence from the Yangtze River Economic Belt

As China's economy began transitioning from one focused on high-speed growth to one focusing on high-quality development, sustainable green development has become the main goal pursued by the government. This study empirically measures the marginal impact of per capita GDP, technological innovation level, industrial structure, openness, fiscal decentralization, and urbanization level on per capita wastewater discharge in 11 provinces (cities) along the Yangtze River Economic Belt (YREB) from 2008 to 2018 using a quantile model. The key findings were as follows: (1) factors such as the per capita GDP, industrial structure, foreign direct investment, and urbanization in the YREB significantly increased water resource pollution; (2) the quantile model regression results showed that the relationship between economic growth and ecological pollution followed the so-called environmental Kuznets inverted U-curve. Wastewater discharge per capita was low in areas with low per capita GDP, meaning that the ecological environment in these areas was more fragile and that the environmental pollution costs due to economic growth were therefore relatively much higher in these areas; (3) fiscal decentralization significantly reduced water resource pollution in relatively developed areas although the effects in the relatively developing areas were not significant; and (4) the effects of technological innovation on reducing water resource pollution in the YREB were positive but not very significant. The results also confirmed that traditional patterns of economic growth increased water pollution in the YREB. For this reason, the government needs to urgently improve policies-for example, upgrading economic structures, preventing over-urbanization, speeding up technological innovation, introducing environmentally friendly foreign investment, and providing more rewards to best practitioners of environmental governance-that is conducive to the achievement of green ecological development.

The Direct and Spillover Effect of Multi-Dimensional Urbanization on PM2.5 Concentrations: A Case Study from the Chengdu-Chongqing Urban Agglomeration in China

The Chengdu-Chongqing urban agglomeration (CUA) faces considerable air quality concerns, although the situation has improved in the past 15 years. The driving effects of population, land and economic urbanization on PM_2.5 concentrations in the CUA have largely been overlooked in previous studies. The contributions of natural and socio-economic factors to PM_2.5 concentrations have been ignored and the spillover effects of multi-dimensional urbanization on PM_2.5 concentrations have been underestimated. This study explores the spatial dependence and trend evolution of PM_2.5 concentrations in the CUA at the grid and county level, analyzing the direct and spillover effects of multi-dimensional urbanization on PM_2.5 concentrations. The results show that the mean PM_2.5 concentrations in CUA dropped to 48.05 μg/m³ at an average annual rate of 4.6% from 2000 to 2015; however, in 2015, there were still 91% of areas exposed to pollution risk (>35 μg/m³). The PM_2.5 concentrations in 92.98% of the area have slowly decreased but are rising in some areas, such as Shimian County, Xuyong County and Gulin County. The PM_2.5 concentrations in this region presented a spatial dependence pattern of "cold spots in the east and hot spots in the west". Urbanization was not the only factor contributing to PM_2.5 concentrations. Commercial trade, building development and atmospheric pressure were found to have significant contributions. The spillover effect of multi-dimensional urbanization was found to be generally stronger than the direct effects and the positive impact of land urbanization on PM_2.5 concentrations was stronger than population and economic urbanization. The findings provide support for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint control strategies of PM_2.5 concentrations, also proving that PM_2.5 pollution control should not only focus on urban socio-economic development strategies but should be an integration of work optimization in various areas such as population agglomeration, land expansion, economic construction, natural adaptation and socio-economic adjustment.

Does the central environmental inspection effectively improve air pollution?-An empirical study of 290 prefecture-level cities in China

In order to control air pollution better, China has adopted the central environmental inspection mechanism. This paper adopts the data of 290 prefecture-level cities in China to evaluate the air improvement effect of the central environmental inspection mechanism. Firstly, based on the Mechanism Design Theory and Principal-agent Theory, this paper analyzes the government incentive measures and effort levels under the conditions of information as symmetry and asymmetry. Then, this paper introduces Regression Discontinuity Design (RDD) Model to evaluate the air improvement effect of the central environmental inspection mechanism. Lastly, this paper explores the sustainability of the environmental inspection mechanism through the regression analysis of environmental inspection revisit. The results show that: under the condition of information symmetry, the central government can make the local governments reach the Pareto optimal effort level through the design of incentive contract. In a short period of time, the first round of environmental inspection and environmental inspection revisit have significantly improved air quality, and significantly reduced PM2.5 and PM10 and other major single pollutants. In general, this system has had an immediate effect. The environmental inspection revisit has also significantly reduced the concentrations of AQI, PM2.5 and PM10. Compared with the first round of environmental inspection and environmental inspection revisit, the latter has a higher level of air pollution reduction and better effect. The sustainability of the improvement effect of environmental inspection mechanism is questioned. It is of great significance to evaluate the actual effect of the central environmental inspection system and tap the unique experience of "Chinese style" to explore how to control air pollution and promote green development.

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing-Tianjin-Hebei Region, China

Heavy-duty diesel trucks (HDDTs) contribute significantly to NO_X and particulate matter (PM) pollution. Although existing studies have emphasized that HDDTs play a dominant role in vehicular pollution, the spatial distribution pattern of HDDT emissions and their related socioeconomic factors are unclear. To fill this research gap, this study investigates the spatial distribution pattern and spatial autocorrelation characteristics of NO_X, PM, and SO₂ emissions from HDDTs in 200 districts and counties of the Beijing-Tianjin-Hebei (BTH) region. We used the spatial lag model to calculate the significances and directions of the pollutants from HDDTs and their related socioeconomic factors, namely, per capita GDP, population density, urbanization rate, and proportions of secondary and tertiary industries. Then, the geographical detector technique was applied to quantify the strengths of the significant socioeconomic factors of HDDT emissions. The results show that (1) NO_X, PM, and SO₂ pollutants emitted by HDDTs in the BTH region have spatial heterogeneity, i.e., low in the north and high in the east and south. (2) The pollutants from HDDTs in the BTH region have significant spatial autocorrelation characteristics. The spatial dependence effect was obvious; for every 1% increase in the HDDT emissions in the surrounding districts and counties, the local HDDT emissions increased by 0.39%. (3) Related factors analysis showed that the proportion of tertiary industries had a significant negative correlation, whereas the proportion of secondary industries and urbanization rate had significant positive correlations with HDDT emissions. Population density and per capita GDP did not pass the significance test. (4) The order of effect intensities of the significant socioeconomic factors was proportion of tertiary industry > proportion of secondary industry > urbanization rate. This study guides scientific decision making for pollution control of HDDTs in the BTH region.