Does national air quality monitoring reduce local air pollution? The case of PM2.5 for China

Unintended mitigation effect of air pollutant regulation on the aquatic cadmium: Evidence from the 11-FYPEP in China

This paper evaluates the unintended mitigation effect of air pollutant regulation on aquatic cadmium (Cd) emissions in the China's Eleventh Five-Year Plan for Environmental Protection (11-FYPEP), by employing a continuous Difference-in-Difference-in-Difference (DDD) estimator. We find that: (1) Although the 11-FYPEP did not target to reduce Cd emission, the implementation of 11-FYPEP reduced the emissions by 2.8 %. (2) The Cd emission is closely related to the industrial level, because the reduction of Cd is 6.1 % higher in areas with lower industrial output, and the mediating effect of the number of industrial enterprises accounts for 6.8 % of the Cd reduction. Based on our findings, implications like improving production efficiency and modifying industrial structure are proposed, as the 11-FYPEP achieves Cd reduction in an unsustainable way.

Does the air pollution level information matter in public perception? Insights from China

Evidence from the past has previously established that the air information disclosure program has an impact on people's air pollution perception. However, the influence of the concrete information communicated has always been overlooked. In this study, we examine the impact of the announced air pollution level information on public pollution perception. We collect air-related Weibo posts from June 1, 2019, to May 31, 2021, in China, and apply a regression discontinuity (RD) design to quantify the impact. According to our research, when a higher pollution level is announced, people's pollution perception expressed online rises by 3.5%-3.7%, which is approximately equivalent to the response to a more than 100 Air Quality Index (AQI) increase. The episode-based analyses reveal that the impact of pollution level information would fade away along with the persistence or the frequency of the announced pollution episode, which shows a phenomenon of information fatigue. The heterogeneity analyses reveal that the impact of pollution level is significant only when the original pollution level is "Good" or "Lightly Polluted", leading to an increase of pollution perception by 2.8% and 4.8%, respectively. We also find that men, highly-educated, and urban residents are more responsive to the pollution level information. Our study illustrates a detailed picture of how people's perception responds to the announced pollution level information under the AQI standards, and can provide guidance for policymakers when developing the AQI standards to maximize the welfare of the air information.

The relationship between dynamic monitoring network plans and eco-efficiency - New evidence from atmospheric quality monitoring policy in China

China's rapid economic development in recent decades has come at a considerable environmental cost. This paper explores whether atmospheric quality monitoring policy (AQMP) improves eco-efficiency by using AQMP as a natural experimental group. We assessed the eco-efficiency of 285 cities in China from 2009 to 2019 using the super-efficient SBM model and estimated the impact of AQMP using the propensity score method Difference-in-Difference (PSM-DID) model. The key findings of this paper are as follows: First, AQMP can enhance eco-efficiency, promoting sustainable urban development. Second, governmental and non-governmental organizations play contrasting roles in either fostering or reversing the positive effects of AQMP. Factors like innovation, clean energy adoption, and industrial structure have a positive mediating influence. Finally, the impact of AQMP on eco-efficiency varies across cities, displaying heterogeneity. Specifically, AQMP has a positive effect on eco-efficiency in resource-rich cities, small and medium-sized urban centers, smart cities, and coastal areas. These findings carry significant implications for the establishment of dynamic monitoring networks and the advancement of eco-efficiency in emerging countries, including China.

Nonlinear effects of socio-economic factors on urban haze in China: Evidence from spatial econometric smooth transition autoregressive regression approach

In recent years, China has achieved numerous economic miracles but it has also been plagued by severe air pollution. The frequent hazy weather has severely restricted China's sustainable development. To investigate the nonlinear threshold effect of socio-economic factors on urban haze in China, this study constructs a spatial econometric Smooth Transition Autoregressive Regression (STAR) model based on the STIRPAT theory by using the remote sensing inversion PM2.5 data of 223 prefecture-level and above cities in China mainland during 2004-2016. In this study, the ARAR-STAR model is estimated by quasi-maximum likelihood estimation, and the accuracy of parameter estimation is verified by Monte Carlo simulation, which proves that the ARAR-STAR model constructed in this study is robust. It is concluded that: there is a complex spatial nonlinear relationship between socio-economic factors such as economic development level, population density, advanced industrial structure, energy consumption, opening-up, and haze pollution. The effect of socio-economic factors on haze emission reduction under the spatial influence has complex heterogeneity with the smooth transition between high and low regimes with economic development. The ARAR-STAR model constructed in this paper, which has both individual fixed effects and time fixed effects, expands the form of existing spatial panel nonlinear models and enriches and implements the application of spatial panel smooth transfer threshold models in the environmental field. Not only can it provide policy recommendations for China to achieve "coordinated efficiency in pollution reduction and carbon reduction" as soon as possible, but it also contributes to China's plan to address global climate change and promote global sustainable development.

Impact of industrial robots on environmental pollution: evidence from China

The application of industrial robots is considered a significant factor affecting environmental pollution. Selecting industrial wastewater discharge, industrial SO2 emissions and industrial soot emissions as the evaluation indicators of environmental pollution, this paper uses the panel data model and mediation effect model to empirically examine the impact of industrial robots on environmental pollution and its mechanisms. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. (2) Industrial robots can reduce environmental pollution by improving the level of green technology innovation and optimizing the structure of employment skills. (3) With the increase in emissions of industrial wastewater, industrial SO2, and industrial dust, the impacts generated by industrial robots are exhibiting trends of a "W" shape, gradual intensification, and progressive weakening. (4) Regarding regional heterogeneity, industrial robots in the eastern region have the greatest negative impact on environmental pollution, followed by the central region, and the western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the emission reduction effect of industrial robots after 2013 is greater than that before 2013. Based on the above conclusions, this paper suggests that the Chinese government and enterprises should increase investment in the robot industry. Using industrial robots to drive innovation in green technology and optimize employment skill structures, reducing environmental pollution.

Can regional joint prevention and control of atmospheric reduce border pollution? Evidence from China's 12th Five-Year Plan on air pollution

Border pollution is usually a difficult problem in environmental governance. Based on the data at the county level in China from 2005 to 2019, this study takes the 12th Five-Year Plan (FYP) for atmospheric pollution as a policy shock, and uses the difference-in-differences (DID) method to explore the impact of regional joint prevention and control (JPC) of atmospheric pollution policy on air pollution of the border regions. Empirical results show that: (1) After implementing the JPC of atmospheric pollution policy, the PM_2.5 concentration in the border regions is reduced by 3.5%. (2) The mechanism analysis shows that there is a spillover effect in the governing behaviors of local governments. In the border areas under low economic growth pressure and high environmental protection pressure, the reduction effect of the JPC of atmospheric pollution policy is more significant on the PM_2.5 concentration of the border regions. The research conclusions have new insights into the role and effect of macro-regional environmental JPC policy and border pollution control, and provide practical guidance for social green governance.

Exposure to ambient air pollution with depressive symptoms and anxiety symptoms among adolescents: A national population-based study in China

BACKGROUND

Air pollution threatens adolescents' physical health and adversely affects adolescents' mental health. Previous studies mostly focused on the effects of air pollution on physical health, but there were few studies on the effects of air pollution on mental health.

METHODS

We collected scores of depressive symptoms and anxiety symptoms from 15,331 adolescents from 43 schools in eleven provinces in September and November 2017. The data on air pollution comes from the China High Air Pollutants dataset, which included concentrations of particulate matter with diameters of ≤1.0 μm (PM₁), diameters of ≤2.5 μm (PM_2.5), and diameters of ≤10 μm (PM₁₀), as well as nitrogen dioxide (NO₂). The associations between air pollution and depressive and anxiety symptoms among adolescents were estimated using generalized linear mixed models.

RESULTS

Depressive and anxiety symptoms among Chinese adolescents were 16% and 32%, respectively. In the adjusted model, an interquartile range (IQR) increase from PM_2.5 was associated with the odds of anxiety symptoms [odds ratio (OR) = 1.01; 95% confidence interval (CI): 1.00, 1.01, P = 0.002]. Also, an IQR increase in PM₁₀ was significantly associated with the odds of anxiety symptoms (OR = 1.01; 95% CI: 1.00, 1.01, P = 0.029). Compared with the lowest quartile, the adjusted OR of anxiety symptoms for the highest quartile of PM_2.5 and PM₁₀ were 1.29 (1.15, 1.44) and 1.23 (1.06, 1.42), respectively. In addition, the association between PM_2.5 and depressive symptoms was significant. The robustness of the results was also confirmed by stratification and sensitivity analyses.

CONCLUSIONS

Exposure values for airborne particulate matter were associated with depressive symptoms and anxiety symptoms in adolescents, particularly for PM_2.5 and PM₁₀ with anxiety symptoms among adolescents.

The role of OFDI in home-country pollution: insights from LMDI and 3SLS approaches

Under the global climate crisis, harnessing investment for sustainable development is a practical and effective measure for international society. Based on the logarithmic mean Divisia index (LMDI) decomposition and three-stage least squares (3SLS) structural approaches, this study explores the home-country pollution reduction effect of Chinese OFDI activities using the city-level panel data from 2007 to 2019. The findings of this study indicate that (1) China has made a remarkable achievement in PM2.5 pollution reduction and governance, especially from the year 2012. (2) The OFDI activities can significantly decrease the home-country PM_2.5 pollution. With every 1% increase in OFDI flows, the overall pollution level will decrease by 0.76%. (3) Compared with the scale mechanism, the technology and composition mechanism effects of OFDI flows are more evident in addressing the home-country PM_2.5 pollution. With several related policy implications, this study may fill the lacuna of how to play the role of OFDI activities in the home country, thus promoting sustainable development in the next stage.

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM_2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM_2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM_2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM_2.5 concentrations. We find that PM_2.5 concentrations in Mid-Eastern China during these years is mainly characterized by "north-south superiority and central inferiority", seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM_2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM_2.5 concentrations drop by more than 30 %. By 2021, PM_2.5 concentrations rebound slightly, rising by 10 % in most provinces.

Is air pollution politics or economics? Evidence from industrial heterogeneity

This paper checks the asymmetrical impact of Beijing's and Shanghai's air quality (AQ) on cross-industries stock returns (SR) by using the quantile-on-quantile (QQ) regression method. The major empirical findings as shown as followings. There are heterogeneous responses from SR to AQ within the same city. Different links are discovered for Beijing and Shanghai within the same industry. Air pollution does not have political or economic properties for all industries. Our research provides useful contributions compared with past literature. First of all, we distinguish whether air pollution is political or economic. Apart from psychology and physiology, government intervention and economic expectation are also important components in interpreting the influence from AQ to SR. Second, this study adequately considers the heterogeneity of industries. Industries differently react to the identical extrinsic shock, depending on the nature of their industry. Besides, the QQ approach captures quantile-varying relationship between variables, and does not need to consider structural fracture and time lag effects. The practical significance is that investors need to focus on national industrial policies, and avoiding biased decisions in stock market from air pollution.

Exploring the detailed spatiotemporal characteristics of PM2.5: Generating a full-coverage and hourly PM2.5 dataset in the Sichuan Basin, China

Fine particulate matter (PM_2.5) has received worldwide attention due to its threat to public health. In the Sichuan Basin (SCB), PM_2.5 is causing heavy health burdens due to its high concentrations and population density. Compared with other heavily polluted areas, less effort has been made to generate a full-coverage PM_2.5 dataset of the SCB, in which the detailed PM_2.5 spatiotemporal characteristics remain unclear. Considering commonly existing spatiotemporal autocorrelations, the top-of-atmosphere reflectance (TOAR) with a high coverage rate and other auxiliary data were employed to build commonly used random forest (RF) models to generate accurate hourly PM_2.5 concentration predictions with a 0.05° × 0.05° spatial resolution in the SCB in 2016. Specifically, with historical concentrations predicted from a spatial RF (S-RF) and observed at stations, an alternative spatiotemporal RF (AST-RF) and spatiotemporal RF (ST-RF) were built in grids with stations (type 1). The predictions from the AST-RF in grids without stations (type 2) and observations in type 1 formed the PM_2.5 dataset. The LOOCV R², RMSE and MAE were 0.94/0.94, 8.71/8.62 μg∕m³ and 5.58/5.57 μg∕m³ in the AST-RF/ST-RF, respectively. Using the produced dataset, spatiotemporal analysis was conducted for a detailed understanding of the spatiotemporal characteristics of PM_2.5 in the SCB. The PM_2.5 concentrations gradually increased from the edge to the center of the SCB in spatial distribution. Two high-concentration areas centered on Chengdu and Zigong were observed throughout the year, while another high-concentration area centered on Dazhou was only observed in winter. The diurnal variation had double peaks and double valleys in the SCB. The concentrations were high at night and low in daytime, which suggests that characterizing the relationship between PM_2.5 and adverse health outcomes by daily means might be inaccurate with most human activities conducted in daytime.

Impact of distance between corporate registration and monitoring stations on environmental performance - Evidence from air quality monitoring stations

Several countries are adopting vertical environmental regulations (air quality monitoring stations) to control pollution. However, there is a relative lack of research analysing environmental regulations and performance from a geographic distance perspective. This study introduces atmospheric quality monitoring stations as a type of environmental regulation using data from Chinese listed companies from 2010 to 2019 to determine the effect of monitoring station distance on corporate environmental performance and the moderating role of corporate strategy. This analysis yielded the following findings. First, based on institutional and signalling theories, we find that monitoring station distance inhibits environmental performance. Second, disclosure, digital transformation, and environmental strategies can reverse the negative effects of monitoring stations. Third, while market drivers improve the ability to monitor station distances, political corruption hinders this. Fourth, firm heterogeneity analysis tells us that the "crowding out" effect of monitoring station distance is more significant for state-owned enterprises, high-tech firms, and heavy polluters. Finally, we found that the monitoring role of stations can be fully utilised only if they are established within a certain distance from the enterprise. These findings are important for establishing air quality monitoring stations and corporate environmental performance in developing countries, including China.

Evaluating the effects of air pollution control policies in China using a difference-in-differences approach

Air pollution has caused wide concern in China, and many governance policies and plans have been implemented in recent years. Based on counterfactual quasi-natural experiments, we analyzed the implementation effects of autumn and winter air pollution control policies in the Jing-Jin-Ji region and surrounding areas using a difference-in-differences (DID) model. The control group was selected based on geographical and meteorological factors, and we analyzed the impact of the policies on six pollutants. The results show that the policies reduced air pollution overall, but not every pollutant. Due to the policy contribution, the concentrations of PM_2.5 and PM₁₀ in autumn and winter from 2017 to 2018 decreased by 6.9 % and 8.5 %, respectively. The numerical value of PM_2.5, PM₁₀, CO, and AQI in 2018-2019 decreased by 18.2 %, 7.2 %, 13.9 %, and 8.8 %, respectively. The role in the reduction of O₃, SO₂, and NO₂ was not obvious. This work provides a research paradigm for evaluating the effects of atmospheric environment policy which can be applied to other studies and provide references for formulating additional policies.

Environmental benefits of innovation policy: China's national independent innovation demonstration zone policy and haze control

In recent years, China has focused its development on technological innovation, trying to achieve a win-win situation between environmental protection and economic growth, and it has formulated a series of policies to promote technological innovation. Taking China's national independent innovation demonstration zone (NIIDZ) policy as an example, this paper empirically investigates the impact of China's innovation policy on haze pollution by using a difference-in-differences (DID) model. The results show that the NIIDZ policy promotes the governance of urban haze pollution and confirms the applicability of the experimentalist governance model in the practice of innovation policy in developing countries. Dynamic analysis shows that the NIIDZ policy has an experience accumulation effect. This policy can continue to promote haze control for at least 6 years, and the policy effect increases year by year. Action mechanism analysis shows that the NIIDZ policy can inhibit urban haze pollution by promoting urban technological innovation and high-tech industrial agglomeration. The estimation results of the spatial DID model show that the NIIDZ policy not only inhibits haze pollution in NIIDZ cities but also has an inhibitory effect on haze pollution in the surrounding non-NIIDZ cities and the NIIDZ cities, which confirms the positive externality characteristics of policy diffusion theory and environmental governance. The conclusions of this paper have important theoretical value for understanding the ecological effect of innovation policy and provide experience for developing countries to implement an experimentalist governance model.

Can vertical environmental regulation induce enterprise green innovation? A new perspective from automatic air quality monitoring station in China

Based on panel data of listed companies in China from 2006 to 2020, this study takes the establishment of automatic air quality monitoring stations as a quasi-natural experiment and uses the staggered difference-in-differences method to explore whether the establishment of monitoring stations promotes green innovation of listed companies. The empirical results show that: (1) The green innovation of companies achieves an increase of 3.5% with monitoring stations in their locations, and an increase of 2.3% with the establishment of each additional monitoring station. This conclusion is valid after a series of robustness tests and exclusive tests. (2) The heterogeneity analyses show that monitoring stations have a greater role in promoting green innovation for non-state-owned enterprises, enterprises in heavy polluting industries and enterprises in key cities for environmental protection. (3) The transmission mechanism test results show that the establishment of automatic air monitoring station has crowding-out effect rather than leverage effect on green innovation, substantial innovation rather than strategic innovation. (4) The further analyses manifest the promotion of end-to-end green innovation, independent invention and quality of green patents.

Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.

Implications of COVID-19 on global environmental pollution and carbon emissions with strategies for sustainability in the COVID-19 era

The impacts of COVID-19 on global environmental pollution since its onset in December 2019 require special attention. The rapid spread of COVID-19 globally has led countries to lock down cities, restrict traffic travel and impose strict safety measures, all of which have implications on the environment. This review aims to systematically and comprehensively present and analyze the positive and negative impacts of COVID-19 on global environmental pollution and carbon emissions. It also aims to propose strategies to prolong the beneficial, while minimize the adverse environmental impacts of COVID-19. It systematically and comprehensively reviewed more than 100 peer-reviewed papers and publications related to the impacts of COVID-19 on air, water and soil pollution, carbon emissions as well as the sustainable strategies forward. It revealed that PM_2.5, PM₁₀, NO₂, and CO levels reduced in most regions globally but SO₂ and O₃ levels increased or did not show significant changes. Surface water, coastal water and groundwater quality improved globally during COVID-19 lockdown except few reservoirs and coastal areas. Soil contamination worsened mainly due to waste from the use of personal protective equipment particularly masks and the packaging, besides household waste. Carbon emissions were reduced primarily due to travel restrictions and less usage of utilities though emissions from certain ships did not change significantly to maintain supply of the essentials. Sustainable strategies post-COVID-19 include the development and adoption of nanomaterial adsorption and microbial remediation technologies, integrated waste management measures, "sterilization wave" technology and energy-efficient technologies. This review provides important insight and novel coverage of the environmental implications of COVID-19 in more than 25 countries across different global regions to permit formulation of specific pollution control and sustainability strategies in the COVID-19 and post-COVID-19 eras for better environmental quality and human health.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

How Air Quality Affect Health Industry Stock Returns: New Evidence From the Quantile-on-Quantile Regression

This paper discusses the asymmetric effect of air quality (AQ) on stock returns (SR) in China's health industry through the quantile-on-quantile (QQ) regression method. Compared to prior literature, our study provides the following contributions. Government intervention, especially industrial policy, is considered a fresh and essential component of analyzing frameworks in addition to investors' physiology and psychology. Next, because of the heterogeneous responses from different industries to AQ, industrial heterogeneity is thus considered in this paper. In addition, the QQ method examines the effect of specific quantiles between variables and does not consider structural break and temporal lag effects. We obtain the following empirical results. First, the coefficients between AQ and SR in the health service and health technology industries change from positive to negative as AQ deteriorates. Second, AQ always positively influences the health business industry, but the values of the coefficients are larger in good air. In addition, different from other industries, the coefficients in the health equipment industry are negative, but the values of the coefficients change with AQ. The conclusions provide important references for investors and other market participants to avoid biased decisions due to poor AQ and pay attention to government industrial policies.