The influence of environmental efficiency on PM2.5 pollution: Evidence from 283 Chinese prefecture-level cities

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Governance matters: Urban expansion, environmental regulation, and PM2.5 pollution

Increasing PM2.5 pollution in urban expansion threatens citizens' health. Environmental regulation has proven to be an effective tool to directly combat PM2.5 pollution. However, whether it can moderate the impacts of urban expansion on PM2.5 pollution, in the context of rapid urbanization, is an interesting and unexplored topic. Therefore, this paper constructs a Drivers-Governance-Impacts framework and explores in depth the interactions among urban expansion, environmental regulation, and PM2.5 pollution. Based on 2005-2018 sample data from the Yangtze River Delta region, the estimation results of the Spatial Durbin model imply that (1) urban expansion has an inverse U-shaped association with PM2.5 pollution. The positive correlation may reverse when the ratio of urban built-up land area hits 0.21. (2) Of the three environmental regulations, investment in pollution control has little impact on PM2.5 pollution. Pollution charges and public attention exhibit a U-shaped and inverted U-shaped relationship with PM2.5 pollution, respectively. (3) In terms of moderating effects, pollution charges can exacerbate PM2.5 pollution from urban expansion, while public attention can inhibit it through its monitoring role. Therefore, we suggest that cities adopt differentiated strategies of urban expansion and environmental protection according to their urbanization levels. Meanwhile, appropriate formal regulation and strong informal regulation will help improve air quality.

Decomposition, decoupling, and future trends of environmental effects in the Beijing-Tianjin-Hebei region: A regional heterogeneity-based analysis

The green growth of Beijing-Tianjin-Hebei (BTH) urban agglomeration plays a leading and exemplary role in overcoming internal resource restrictions, addressing climate change, and supporting China's high-quality growth. From the standpoint of pollution reduction and carbon reduction, this paper first conducts a comprehensive evaluation of the environmental impact based on combined weighting technique. The Logarithmic Mean Divisia Index (LMDI) model is used to decompose the environmental impact drivers in distinct areas. A decoupling effort index is further constructed to measure the effect of various efforts on the decoupling of economic growth and environmental impact, the improved grey Markov model is applied to predict the future trend of regional decoupling efforts. The results of empirical analysis based on data of the BTH region during 2011-2018 show that: 1) the environmental impact index of Beijing is the lowest followed by Tianjin and Hebei; 2) environmental regulation exerts the most significant impact on reducing environmental pressure in Beijing while technology progress and energy intensity have the most significant effect on easing environmental pressure in Tianjin; 3) strong decoupling efforts have been found in Beijing, Tianjin and Hebei, however, such effect is more significant in Beijing; 4) Beijing's decoupling state is mostly driven by regulatory effect, intensity effect, and scale effect, while Tianjin and Hebei's decoupling states are primarily driven by improvements in environmental regulation and energy intensity; 5) according to the forecast outcome of the improved grey Markov technique, a state of strong decoupling effort will be maintained in the BTH area by 2025, and the decoupling effort index in Beijing will remain the highest while the index in Hebei will remain the lowest.

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.

How can urban administrative boundary expansion affect air pollution? Mechanism analysis and empirical test

As a momentous policy tool for spatial management, urban administrative boundary expansion (UABE) significantly impacts resource reorganization and development modes. However, the environmental effects of UABE are easily ignored. Whether UABE can also impove the environmental quality in addition to its economic effects remains to be answered. To fill this gap, we took the city-county merger policy (CCMP) in China as quasi-experimental evidence and empirically investigated the impacts of CCMP on air pollution based on the difference-in-difference method. The impact mechanisms were also analyzed from a whole-process perspective. The results demonstrate that, apart from expanding the urban scale, UABE can improve urban air quality as well. Further analysis shows that the positive effect is mainly realized by source control and process management, rather than end-of-pipe treatment. Besides, the impacts of UABE on air pollution exhibit obvious spatial heterogeneous characteristics. We also reported that the environmental effects of UABE are largely dependent on governmental control, rather than market factors. According to the results above, flexible administrative boundary adjustment, strict government regulations, and effective market systems are required to realize the dual goals of environmental improvement and space optimization.

Unraveling the dynamics, heterogeneity, determinants of eco-efficiency in Beijing-Tianjin-Hebei urban agglomeration, China

Eco-efficiency has been considered a valuable gauge for evaluating how efficient economic activities are in regard to resource inputs and eco-environmental pressures. Even though Ecosystem services (ESs) are inseparable from sustainable eco-environment, a paucity of literature has considered ESs in eco-efficiency research lines. Therefore, this study aims to construct a novel eco-efficiency evaluation framework by integrating ESs as natural capital input and measure it utilizing the Epsilon-based measure model for the county-level cities in Beijing-Tianjin-Hebei urban agglomeration (BTHUA) during the period 2005-2015. The spatial econometric technique is further performed to acquire quantitative evidence about whether ESs and other determinants impact eco-efficiency. The results revealed that eco-efficiency increased continuously in the whole BTHUA and BTHUA's optimized development functional areas, whereas eco-efficiency of BTHUA's sub-regions showed a significant temporal diversity. The average eco-efficiency values of cities in key development functional areas and restricted development functional areas showed the V-shaped trend (declining before 2010 and then rising). Interestingly, this study found that ESV economic loss may result in eco-efficiency decline for cities located in key development functional areas. From the spatial heterogeneity perspective, the city with high EE is mainly located in eastern BTHUA, whereas cities in the northern plateau areas, southwestern, and western BTHUA have relatively low EE. Furthermore, there existed a significant spatial autocorrelation and a spatial agglomeration heterogeneity, which suggests that the low-low correlation regions gradually being the most dominant spatial pattern. The results of spatial econometric model verified that water yield has the strongest positive effect on EE while soil erosion will lead to declining EE. This paper potentially provides new insights for future policy design of urban agglomeration sustainable deployment.

Effects of industrial green total factor energy efficiency on haze abatement: A spatial econometric analysis based on China's 272 cities

Environmental issues represented by haze pollution have become the focus of all sectors of society. Industrial activities, especially industrial energy consumption play an important role in it. After the energy consumption of provincial industrial sectors is decomposed into urban industrial sectors through nighttime light data, this study measures the industrial green total factor energy efficiency (IGTFEE) of Chinese cities from 2003 to 2017, and the haze abatement effect of IGTFEE is investigated from the perspective of spatial correlation. The results show that China's haze pollution and IGTFEE present a positive spatial cluster. Additionally, China's haze pollution has obvious path dependence that shows a monotonous decreasing trend as the aggravation of haze pollution. Meanwhile, although haze pollution shows a serious leakage effect, it plays a warning role in haze control in surrounding areas. This effect is stronger in the most severely polluted areas that have higher economic levels. Not only does the improvement of IGTFEE have a positive effect on local haze abatement but can control haze pollution in spatially related areas through spillover effects and spatial feedback effects. However, the effects are significantly heterogeneous and asymmetric across quantiles, where IGTFEE has a marginal diminishing distribution on haze abatement at the middle and low quantiles, but the haze pollution has intensified at the high quantiles due to the energy rebound effect. The results also reveal that haze abatement conforms to the spatial EKC hypothesis in China. Meanwhile, industrial agglomeration, environmental regulations, and industrial upgrading are vital drivers for haze abatement.

Technology imports, independent innovation, and China's green economic efficiency: an analysis based on spatial and mediating effect

Environmental quality and economic growth are important factors that need to be balanced for sustainable development, especially in developing countries where technology is relatively backward. Many studies have shown that technology imports may be beneficial to economic growth, but once the resources and environment are taken into consideration, the role of technology imports becomes blurred. Based on provincial panel data of China from 2004 to 2019, this paper investigates the influence mechanism of domestic and foreign technology imports on the green economic efficiency (GEE) of 30 provinces in China. There are two main conclusions: First, GEE is spatially related and the impact of technology imports on GEE has a significant spillover effect. Besides, the relationship between technology imports and GEE is non-linear, both in terms of direct and indirect effects. Second, independent innovation plays an important role in the influence mechanism of technology imports on GEE. As the level of independent innovation increased, the impact of technology imports on GEE turns from negative to positive, and it is strengthened through the channel of "transfer-absorption-diffusion-re-innovation." In this regard, some measures should be taken to enhance the role of technology imports in improving GEE.

The role of public-private partnership investment and eco-innovation in environmental abatement in USA: evidence from quantile ARDL approach

The current research investigates the role of public-private partnership investment (PPPI) and ecological innovation (ECO) along with economic growth on the environmental abatement (i.e., carbon dioxide emission, particulate matters 2.5) in the USA. Quantile autoregressive lagged (QARDL) method was employed during the study period of 1990-2015. The study findings confirm that under long-run estimation, GDP and PPPI are causing more environmental abatement in the form of CO2 emission and haze pollution like PM2.5. The factors like ecological innovation and GDP2 are playing their role towards lowering the CO2 emission and PM2.5 under different quantiles. Furthermore, it is observed that under short-run estimation, past values of the carbon emissions and PM2.5 have their significant and positive relationship with their current values. Besides, the findings through Wald test estimation confirm that parameter constancy of the speed of adjustment parameter is rejected at 1% significance level for the CO2 emission and haze pollution like PM2.5 in USA. Besides, present study also provides some policy implications.

A step towards sustainable environment in China: The role of eco-innovation renewable energy and environmental taxes

In the contemporary environment, developing countries are more focused on how economic factors can reasonably utilize technological advancement and carbon neutrality target as effective mechanisms in achieving sustainable production and consumption patterns. The effort to attain carbon neutrality target on natural environment in terms of lower carbon emission (CO2), haze pollution, and greenhouse gas (GHG) requires measures like the usage of non-renewable energy, ecological innovation, and environmental taxes. In doing so, this study considers the sustainability of China's natural environment in terms of CO2 emission, haze pollution through PM2.5, and greenhouse gas emission as well as factors like ecological innovation (ECO), environmental taxes (ERT), renewable energy, and globalization as the key determinants. The Quantile ARDL approach was used to examine both long- and short-run relationships between the explanatory and outcome variables. The results confirmed that there is a significant and negative impact of ECO, renewable energy, and ERT on CO2 emission in the region of China under different quantiles. Whereas, the globalization factor was observed as positively and significantly linked with CO2 emission but only for the higher quantiles. The long-run estimation further showed that ECO, renewable energy, and ERT can significantly help to decrease haze pollution in terms of PM2.5 in China. Furthermore, QARDL also confirms the negative and long-run estimation between the ECO, REN, and ERT, whereas globalization is causing more GHG in China, subsequently creating more environmental sustainability issues. Thus, it is concluded that effective innovation, renewable energy consumption, and environmental taxes reduce carbon emission while globalization increases the carbon emission in the country.

Does Environmental Policy Affect Green Total Factor Productivity? Quasi-Natural Experiment Based on China's Air Pollution Control and Prevention Action Plan

It is the scientific way to promote the transformation and optimization of an industrial structure to promote the improvement of its green total factor productivity (GTFP) by formulating environmental regulation policies. Based on the GTFP panel data of 30 provinces in China from 2004 to 2017, this paper takes the “Air Pollution Control and Prevention Action Plan” (APCP Action Plan) as the proxy dummy variable of environmental regulation, and uses the difference-in-differences (DID) model to explore the impact of the implementation of the APCP Action Plan on GTFP. In addition, by constructing the industrial structure optimization index, this paper analyzes how the APCP Action Plan policy affects GTFP through the transformation and optimization of industrial structure. The following basic conclusions are obtained: First, environmental regulation policies like the APCP Action Plan can improve GTFP. Second, the APCP Action Plan has regional heterogeneity in promoting GTFP in different regions. The policy only significantly affects the GTFP in the Pearl River Delta region in southern China. Third, the “quantity” and “quality” of the optimization of industrial structure will weaken the promoting effect of the APCP Action Plan on GTFP. In contrast, the rationalization of industrial structure will aggravate this promoting effect.

Improvement of low temperature carbon combustion catalyst characteristic caused by mixing Bi2O3 with Tl2O3

This study investigated the addition of various oxides to further improve the catalytic characteristics of Tl₂O₃, which offers a high carbon combustion catalytic capacity to lower the carbon combustion temperature of 660 °C by ~ 300 °C. Mixtures of carbon (2 wt%) with composite catalysts comprising 20 wt% Tl₂O₃–80wt% added oxide were analyzed using DSC. Bi₂O₃ offered the best improvement, where the exothermic peak temperatures for carbon combustion of carbon with various Tl₂O₃–x wt% Bi₂O₃ composites were lower than that of carbon with pure Tl₂O₃. Isothermal TG measurements were performed using a mixture of carbon and the Tl₂O₃‒95 wt% Bi₂O₃ composite catalyst, where a 2 wt% weight loss (i.e. removal of all carbon) was achieved above 230 °C. A porous alumina filter was coated with the composite catalyst and carbon was deposited on the filter surface. The filter was held at constant temperatures under air flow, which confirmed that carbon was completely removed at 230 °C. This study demonstrated the potential for using these composite catalysts in self-cleaning particulate filters to decompose and eliminate fine particulate matter and diesel particulate matter generated from steelworks, thermal power plants, and diesel vehicles simply using the heat of the exhaust gas in a factory flue-gas stack or vehicle muffler.

A grey spatiotemporal incidence model with application to factors causing air pollution

The factors causing air pollution in China has caused extensive concern, but there are still many problems in the grey incidence model of identifying air pollution factors. The results produced by the existing grey incidence models are not stable when the order of objects in a given panel data is changed. In order to improve the reliability and uniformity of the grey incidence model, a new grey incidence model, called the grey spatiotemporal incidence model, abbreviated as the GSTI model, is designed in this paper. In the proposed model, the spatiotemporal data which can represent the spatial relationship among different objects rather than the three-dimensional panel data are defined. In addition, the new model includes two procedures. Firstly, the trend coefficient is used to measure the positive and negative connections between two data sequences. Secondly, the measurement coefficient is utilized to calculate the size of grey incidence degree. Subsequently, five properties of the GSTI model are discussed. To demonstrate its practicability and compatibility, the novel model is utilized to identify south Jiangsu province's main factors causing air pollution according to monthly data for 2018. The abundant comparison shows the applicability and superiority of the model in the identification of air pollution factors and the construction of grey incidence model.